Measuring the complexity of social associations using mixture models

This is final version. Available on open access from Springer via the DOI in this record.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 data sets. The method could be extended for use on interaction rate data using Poisson mixture models, or on multidimensional relationship data using multivariate mixture models

Interictal epileptiform discharges have an independent association with cognitive impairment in children with lesional epilepsy

OBJECTIVES: The relative contribution of interictal epileptiform discharges (IEDs) to cognitive dysfunction in comparison with the underlying brain pathology is not yet understood in children with lesional focal epilepsy. METHODS: The current study investigated the association of IEDs with intellectual functioning in 103 children with medication-resistant focal epilepsy. Hierarchical multiple regression analyses were used to determine the independent contribution of IED features on intellectual functioning, after controlling for effects of lesional pathology, epilepsy duration, and medication. Exploratory analyses were conducted for language and memory scores as well as academic skills available in a subset of participants. RESULTS: The results reveal that IEDs have a negative association with IQ with independent, additive effects documented for frequent and bilaterally distributed IEDs as well as discharge enhancement in sleep. Left-lateralized IEDs had a prominent effect on verbal intelligence, in excess of the influence of left-sided brain pathology. These effects extended to other cognitive functions, most prominently for sleep-enhanced IEDs to be associated with deficits in expressive and receptive language, reading, spelling and numerical skills. SIGNIFICANCE: Overall, IED effects on cognition were of a magnitude similar to lesional influences or drug effects (topiramate use). This study demonstrates an association between IEDs and cognitive dysfunction, independent of the underlying focal brain pathology

Diversity and consequences of social network structure in toothed whales

This is the final version. Available on open access from Frontiers Media via the DOI in this recordData availability statement: The database of studies generated for this study can be found in the supplemental material.Toothed whales (suborder Odontoceti) are highly social, large brained mammals with diverse social systems. In recent decades, a large body of work has begun investigating these dynamic, complex societies using a common set of analytical tools: social network analysis. The application of social network theory to toothed whales enables insight into the factors that underlie variation in social structure in this taxon, and the consequences of these structures for survival, reproduction, disease transmission, and culture. Here, we perform a systematic review of the literature regarding toothed whale social networks to identify broad patterns of social network structure across species, common drivers of individual social position, and the consequences of network structure for individuals and populations. We also identify key knowledge gaps and areas ripe for future research. We recommend that future studies attempt to expand the taxonomic breadth and focus on standardizing methods and reporting as much as possible to allow for comparative analyses to test evolutionary hypotheses. Furthermore, social networks analysis may provide key insights into population dynamics as indicators of population health, predictors of disease risk, and as direct drivers of survival and reproduction.Natural Environment Research Council (NERC

Mortality risk and social network position in southern resident killer whales (Orcinus orca): sex differences and the importance of salmon abundance

An individual\u27s ecological environment affects its mortality risk, which in turn has fundamental consequences for life-history evolution and population viability. In many species, social relationships are likely to be an important component of an individual\u27s environment, and therefore mortality risk. Here, we examine the relationship between social position and mortality risk in Southern resident killer whales (Orcinus orca) using over three decades of social and demographic data. We find that the social position of male, but not female, killer whales in their social unit predicts their mortality risk. More socially integrated males have a significantly lower risk of mortality than socially peripheral males, particularly in years of low salmon abundance, suggesting that social position mediates access to resources. Male killer whales are larger and require more resources than females, increasing their vulnerability to starvation in years of low salmon abundance. More socially integrated males are likely to have better access to social information and food-sharing opportunities which may enhance their survival in years of low prey abundance. Our results show that observable variation in the social environment is linked to variation in mortality risk. Furthermore, our results highlight the importance of considering sex differences in social effects on survival when developing conservation strategies for long-lived social mammals

Measuring the complexity of social associations using mixture models

We propose a method for examining and measuring the complexity of animal social networks that are characterized using association indices. The method focuses 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 data sets. The method could be extended for use on interaction rate data using Poisson mixture models, or on multidimensional relationship data using multivariate mixture models

Calculating effect sizes in animal social network analysis

This is the final version. Available on open access from Wiley via the DOI in this record1. 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. 2. 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. 3. 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. 4. We recommend this approach should be adopted as standard practice in the statistical analysis of animal social network dataNatural Environment Research Council (NERC

The evolution of menopause in toothed whales

This is the final version. Available on open access from Nature Research via the DOI in this recordData availability: All data used in this analysis are available as a database at: github.com/samellisq/marinelifehistdataCode availability: All R and stan code used for this analysis are available at osf.io/26s7m/. In addition, the mortality model is implemented as an R package available from: github.com/samellisq/marinesurvivalUnderstanding how and why menopause has evolved is a long-standing challenge across disciplines. Females can typically maximize their reproductive success by reproducing for the whole of their adult life. In humans, however, women cease reproduction several decades before the end of their natural lifespan1,2. Although progress has been made in understanding the adaptive value of menopause in humans3,4, the generality of these findings remains unclear. Toothed whales are the only mammal taxon in which menopause has evolved several times5, providing a unique opportunity to test the theories of how and why menopause evolves in a comparative context. Here, we assemble and analyse a comparative database to test competing evolutionary hypotheses. We find that menopause evolved in toothed whales by females extending their lifespan without increasing their reproductive lifespan, as predicted by the 'live-long' hypotheses. We further show that menopause results in females increasing their opportunity for intergenerational help by increasing their lifespan overlap with their grandoffspring and offspring without increasing their reproductive overlap with their daughters. Our results provide an informative comparison for the evolution of human life history and demonstrate that the same pathway that led to menopause in humans can also explain the evolution of menopause in toothed whales.Leverhulme TrustNatural Environment Research Council (NERC

Importance of old bulls: Leaders and followers in collective movements of all-male groups in African savannah elephants (Loxodonta africana)

This is the final version. Available on open access from Nature Research via the DOI in this record.Due to the sensitive nature of reporting on elephant locations and numbers, the data that support the findings of this study are available on reasonable request from the corresponding author.In long-lived social species, older individuals can provide fitness benefits to their groupmates through the imparting of ecological knowledge. Research in this area has largely focused on females in matrilineal societies where, for example, older female African savannah elephants (Loxodonta africana) are most effective at making decisions crucial to herd survival, and old post-reproductive female resident killer whales (Orcinus orca) lead collective movements in hunting grounds. In pathways in Makgadikgadi Pans National Park, Botswana, we found that the oldest males were more likely to lead collective movements. Our results challenge the assumption that older male elephants are redundant in the population and raise concerns over the biased removal of old bulls that currently occurs in both legal trophy hunting and illegal poaching. Selective harvesting of older males could have detrimental effects on the wider elephant society through loss of leaders crucial to younger male navigation in unknown, risky environments

Common datastream permutations of animal social network data are not appropriate for hypothesis testing using regression models

This is the final version. Available on open access from Wiley via the DOI in this record1. 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 animal social networks. In particular, they have been used to test whether exogenous factors are related to network structure by interfacing these permutations with regression models. 2. 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. 3. Our simulations show that, if used to indicate whether a relationship exists between network structure and a covariate, datastream permutations can result in extremely high type I error rates, in some cases approaching 50%. In the same set of simulations, traditional node-label permutations produced appropriate type I error rates (~ 5%). 4. Our analysis shows that datastream permutations do not represent the appropriate null hypothesis for these analyses. We suggest that potential alternatives to this procedure may be found in regarding the problems of non-independence of network data and unreliability of observations separately. If biases introduced during data collection can be corrected, either prior to model fitting or within the model itself, node-label permutations then serve as a useful test for interfacing animal social network analysis with regression modellingNatural Environment Research Council (NERC)NI