The adaptive value of density-dependent habitat specialization and social network centrality

Density dependence is a fundamental ecological process. In particular, animal habitat selection and social behavior often affect fitness in a density-dependent manner. The Ideal Free Distribution (IFD) and niche variation hypothesis (NVH) present distinct predictions associated with Optimal Foraging Theory about how the effect of habitat selection on fitness varies with population density. Using caribou (Rangifer tarandus) in Canada as a model system, we test competing hypotheses about how habitat specialization, social behavior, and annual reproductive success (co)vary across a population density gradient. Within a behavioral reaction norm framework, we estimate repeatability, behavioral plasticity, and covariance among social behavior and habitat selection to investigate the adaptive value of sociality and habitat selection. In support of NVH, but not the IFD, we find that at high density habitat specialists had higher annual reproductive success than generalists, but were also less social than generalists, suggesting the possibility that specialists were less social to avoid competition. Our study supports niche variation as a mechanism for density-dependent habitat specialization.

Spatiotemporal-social association predicts immunological similarity in rewilded mice

Environmental influences on immune phenotypes are well-documented, but our understanding of which elements of the environment affect immune systems, and how, remains vague. Behaviors, including socializing with others, are central to an individual's interaction with its environment. We therefore tracked behavior of rewilded laboratory mice of three inbred strains in outdoor enclosures and examined contributions of behavior, including associations measured from spatiotemporal co-occurrences, to immune phenotypes. We found extensive variation in individual and social behavior among and within mouse strains upon rewilding. In addition, we found that the more associated two individuals were, the more similar their immune phenotypes were. Spatiotemporal association was particularly predictive of similar memory T and B cell profiles and was more influential than sibling relationships or shared infection status. These results highlight the importance of shared spatiotemporal activity patterns and/or social networks for immune phenotype and suggest potential immunological correlates of social life.

Bullying as an advertisement of social dominance in common waxbills

Bullying consists of preferentially attacking individuals lowest in the dominance hierarchy, and its functions are unclear because the most subordinate individuals do not pose social challenges to the aggressor. Instead, conflict is expected mostly between individuals of similar dominance rank or socially distant (i.e. weakly associated), among whom dominance relationships may not be well established. A possible function of bullying is that it may be used as a low-risk strategy of showing-off dominance to relevant third parties. To study this hypothesis, we monitored aggressions during feeding, the composition of audiences, dominance hierarchy and social network of common waxbills (Estrilda astrild) in an open-air mesocosm, and tested (i) whether their aggressions show a pattern of bullying, and (ii) whether audience effects influence aggressiveness. Waxbills showed bullying, most often attacking the lowest ranking individuals rather than socially distant individuals or those of similar dominance rank, and aggressions increased when the audience included socially distant individuals, indicating a signalling function of bullying. Showing-off dominance in the presence of socially distant individuals may be a strategy to manage dominance hierarchies, avoiding direct fights with potentially dangerous opponents in the audience. We suggest that bullying is a safe manner of managing dominance hierarchies, by signalling dominance status to potential opponents.

Familiarity, age, weaning and health status impact social proximity networks in dairy calves

Social network analysis in dairy calves has not been widely studied, with previous studies limited by the short study duration, and low number of animals and replicates. In this study, we investigated social proximity interactions of 79 Holstein-Friesian calves from 5 cohorts for up to 76 days. Networks were computed using 4-day aggregated associations obtained from ultrawideband location sensor technology, at 1 Hz sampling rate. The effect of age, familiarity, health, and weaning status on the social proximity networks of dairy calves was assessed. Networks were poorly correlated (non-stable) between the different 4-day periods, in the majority of them calves associated heterogeneously, and individuals assorted based on previous familiarity for the whole duration of the study. Age significantly increased association strength, social time and eigenvector centrality and significantly decreased closeness and coefficient of variation in association (CV). Sick calves had a significantly lower strength, social time, centrality and CV, and significantly higher closeness compared to the healthy calves. During and after weaning, calves had significantly lower closeness and CV, and significantly higher association strength, social time, and eigenvector centrality. These results indicate that age, familiarity, weaning, and sickness have a significant impact on the variation of social proximity interaction of calves.

Common permutation methods in animal social network analysis do not control for non-independence

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.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00265-022-03254-x.

Measuring the Construction Project Resilience from the Perspective of Employee Behaviors

The increasing developmental potentiality for the construction industry brings the huge challenge to make up the limitation of traditional construction project management mode when adapting to the Sustainable Development Goals (SDGs). Due to the high energy and resource consumption for the construction projects, there are a large number of uncertainties and disturbances in achieving resilient urban infrastructure. Studying construction project resilience (CPR) is imperative. However, prior studies preferred to measure resilience by systemic indicators, which are complex and unfriendly. Studying CPR from the perspective of employee behavior (EB) remains rare. Hence, this study proposed a social network analysis (SNA) methodology to overcome the research gap. Firstly, six EBs are identified by the systematic literature review (SLR). Then, the critical employees (CEs) and their interrelationships are investigated to form the social network. Six SNA parameters including density, degree centrality, betweenness centrality, efficiency, constraint, and cliques are selected to model the EBs, namely PMT cohesion, the identity of the project culture, formal behavior between employees, collaboration efficacy, informal social constraints, and reciprocity and mutual trust. Finally, the value of CPR is obtained and the strategies for improving the CPR are proposed from four characteristics: robustness, redundancy, rapidity, and resourcefulness. The findings provided a simple and effective techniques to measure the CPR and could benefit the project manager to improve the CPR by exerting accurate strategies to the EBs in poor performance.

Respiratory Disease Risk of Zoo-Housed Bonobos Is Associated with Sex and Betweenness Centrality in the Proximity Network

Infectious diseases can be considered a threat to animal welfare and are commonly spread through both direct and indirect social interactions with conspecifics. This is especially true for species with complex social lives, like primates. While several studies have investigated the impact of sociality on disease risk in primates, only a handful have focused on respiratory disease, despite it being a major cause of morbidity and mortality in both wild and captive populations and thus an important threat to primate welfare. Therefore, we examined the role of social-network position on the occurrence of respiratory disease symptoms during one winter season in a relatively large group of 20 zoo-housed bonobos with managed fission-fusion dynamics. We found that within the proximity network, symptoms were more likely to occur in individuals with higher betweenness centrality, which are individuals that form bridges between different parts of the network. Symptoms were also more likely to occur in males than in females, independent of their social-network position. Taken together, these results highlight a combined role of close proximity and sex in increased risk of attracting respiratory disease, two factors that can be taken into account for further welfare management of the species.

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

Permutation tests are widely used to test null hypotheses with animal social network data, but suffer from high rates of type I and II error when the permutations do not properly simulate the intended null hypothesis.

Two common types of permutations each have limitations. Pre‐network (or datastream) permutations can be used to control ‘nuisance effects’ like spatial, temporal or sampling biases, but only when the null hypothesis assumes random social structure. Node (or node‐label) permutation tests can test null hypotheses that include nonrandom social structure, but only when nuisance effects do not shape the observed network.

We demonstrate one possible solution addressing these limitations: using pre‐network permutations to adjust the values for each node or edge before conducting a node permutation test. We conduct a range of simulations to estimate error rates caused by confounding effects of social or non‐social structure in the raw data.

Regressions on simulated datasets suggest that this ‘double permutation’ approach is less likely to produce elevated error rates relative to using only node permutations, pre‐network permutations or node permutations with simple covariates, which all exhibit elevated type I errors under at least one set of simulated conditions. For example, in scenarios where type I error rates from pre‐network permutation tests exceed 30%, the error rates from double permutation remain at 5%.

The double permutation procedure provides one potential solution to issues arising from elevated type I and type II error rates when testing null hypotheses with social network data. We also discuss alternative approaches that can provide robust inference, including fitting mixed effects models, restricted node permutations, testing multiple null hypotheses and splitting large datasets to generate replicated networks. Finally, we highlight ways that uncertainty can be explicitly considered and carried through the analysis.

Age and sex influence social interactions, but not associations, within a killer whale pod

Social structure is a fundamental aspect of animal populations. In order to understand the function and evolution of animal societies, it is important to quantify how individual attributes, such as age and sex, shape social relationships. Detecting these influences in wild populations under natural conditions can be challenging, especially when social interactions are difficult to observe and broad-scale measures of association are used as a proxy. In this study, we use unoccupied aerial systems to observe association, synchronous surfacing, and physical contact within a pod of southern resident killer whales (Orcinus orca). We show that interactions do not occur randomly between associated individuals, and that interaction types are not interchangeable. While age and sex did not detectably influence association network structure, both interaction networks showed significant social homophily by age and sex, and centrality within the contact network was higher among females and young individuals. These results suggest killer whales exhibit interesting parallels in social bond formation and social life histories with primates and other terrestrial social mammals, and demonstrate how important patterns can be missed when using associations as a proxy for interactions in animal social network studies.

Aerial drone observations identified a multilevel society in feral horses

The study of non-human multilevel societies can give us insights into how group-level relationships function and are maintained in a social system, but their mechanisms are still poorly understood. The aim of this study was to apply spatial association data obtained from drones to verify the presence of a multilevel structure in a feral horse society. We took aerial photos of individuals that appeared in pre-fixed areas and collected positional data. The threshold distance of the association was defined based on the distribution pattern of the inter-individual distance. The association rates of individuals showed bimodality, suggesting the presence of small social organizations or "units". Inter-unit distances were significantly smaller than those in randomly replaced data, which showed that units associate to form a higher-level social organization or "herd". Moreover, this herd had a structure where large mixed-sex units were more likely to occupy the center than small mixed-sex units and all-male-units, which were instead on the periphery. These three pieces of evidence regarding the existence of units, unit association, and stable positioning among units strongly indicated a multilevel structure in horse society. The present study contributes to understanding the functions and mechanisms of multilevel societies through comparisons with other social indices and models as well as cross-species comparisons in future studies.