Stakeholders and social networks identify potential roles of communities in sustainable management of invasive species

Identifying stakeholders and analysing the pattern of relationships among them are important steps toward collaborating with individuals and groups for collective action. The process of stakeholders’ communication can be understood by interpreting the structure of the network in which stakeholders operate. Our study attempted to identify stakeholders, determine the structure of their relationships through a social network analysis and examine how network structure could aid collaborative efforts towards invasive species management. We used organizational network analysis, a web-based program, to collect network data for conservation groups on Waiheke Island, New Zealand. Response rate was 47% of the 103 contacts made and thirty-five conservation groups were identified. Results revealed low density, high non-reciprocity, and high centrality among a few stakeholders in the network suggesting a non-cohesive network. We identify how influential stakeholders could carefully initiate and strengthen collaborations that might lead to collective invasive species management action after a thorough examination of mandated, funded or shared interest relationships.     

Taking sociality seriously: the structure of multi-dimensional social networks as a source of information for individuals

Understanding human cognitive evolution, and that of the other primates, means taking sociality very seriously. For humans, this requires the recognition of the sociocultural and historical means by which human minds and selves are constructed, and how this gives rise to the reflexivity and ability to respond to novelty that characterize our species. For other, non-linguistic, primates we can answer some interesting questions by viewing social life as a feedback process, drawing on cybernetics and systems approaches and using social network neo-theory to test these ideas. Specifically, we show how social networks can be formalized as multi-dimensional objects, and use entropy measures to assess how networks respond to perturbation. We use simulations and natural 'knock-outs' in a free-ranging baboon troop to demonstrate that changes in interactions after social perturbations lead to a more certain social network, in which the outcomes of interactions are easier for members to predict. This new formalization of social networks provides a framework within which to predict network dynamics and evolution, helps us highlight how human and non-human social networks differ and has implications for theories of cognitive evolution.     

Agonistic reciprocity is associated with reduced male reproductive success within haremic social networks

While individual variation in social behaviour is ubiquitous and causes social groups to differ in structure, how these structural differences affect fitness remains largely unknown. We used social network analysis of replicate bluebanded goby (Lythrypnus dalli) harems to identify the reproductive correlates of social network structure. In stable groups, we quantified agonistic behaviour, reproduction and steroid hormones, which can both affect and respond to social/reproductive cues. We identified distinct, optimal social structures associated with different reproductive measures. Male hatching success (HS) was negatively associated with agonistic reciprocity, a network structure that describes whether subordinates ‘reciprocated’ agonism received from dominants. Egg laying was associated with the individual network positions of the male and dominant female. Thus, males face a trade-off between promoting structures that facilitate egg laying versus HS. Whether this reproductive conflict is avoidable remains to be determined. We also identified different social and/or reproductive roles for 11-ketotestosterone, 17β-oestradiol and cortisol, suggesting that specific neuroendocrine mechanisms may underlie connections between network structure and fitness. This is one of the first investigations of the reproductive and neuroendocrine correlates of social behaviour and network structure in replicate, naturalistic social groups and supports network structure as an important target for natural selection.     

A framework to evaluate whether to pool or separate behaviors in a multilayer network

A multilayer network approach combines different network layers,which are connected by interlayer edges,to create a single mathematical object.These networks can contain a variety of information types and represent different aspects of a system.However,the process for selecting which information to include is not always straightforward.Using data on 2 agonistic behaviors in a captive population of monk parakeets(Myiopsitta monachus),we developed a framework for investigating how pooling or splitting behaviors at the scale of dyadic relationships(between 2 individuals)affects individual-and group-level social properties.We designed 2 reference models to test whether randomizing the number of interactions across behavior types results in similar structural patterns as the observed data.Although the behaviors were correlated,the first reference model suggests that the 2 behaviors convey different information about some social properties and should therefore not be pooled.However,once we controlled for data sparsity,we found that the observed measures corresponded with those from the second reference model.Hence,our initial result may have been due to the unequal frequencies of each behavior.Overall,our findings support pooling the 2 behaviors.Awareness of how selected measurements can be affected by data properties is warranted,but nonetheless our framework disentangles these efforts and as a result can be used for myriad types of behaviors and questions.This framework will help researchers make informed and data-driven decisions about which behaviors to pool or separate,prior to using the data in subsequent multilayer network analyses.     

Baboon (Papio anubis) social complexity--a network approach

Although many studies have analyzed the causes and consequences of social relationships, few studies have explicitly assessed how measures of social relationships are affected by the choice of behaviors used to quantify them. The use of many behaviors to measure social relationships in primates has long been advocated, but it was analytically difficult to implement this framework into primatological work. However, recent advances in social network analysis (SNA) now allow the comparison of multiple networks created from different behaviors. Here we use our database of baboon social behavior (Papio anubis, Gashaka Gumti National Park, Nigeria) to investigate (i) to what extent social networks created from different behaviors overlap, (ii) to what extent individuals occupy similar social positions in these networks and (iii) how sex affects social network position in this population of baboons. We used data on grooming, aggression, displacement, mounting and presenting, which were collected over a 15-month period. We calculated network parameters separately for each behavior. Networks based on displacement, mounting and presenting were very similar to each other, whereas grooming and aggression networks differed both from each other and from mounting, displacement and presenting networks. Overall, individual network positions were strongly affected by sex. Individuals central in one network tended to be central in most other networks as well, whereas other measures such as clustering coefficient were found to vary depending on the behavior analyzed. Thus, our results suggest that a baboon's social environment is best described by a multiplex network based on affiliative, aggressive and sexual behavior. Modern SNA provides a number of useful tools that will help us to better understand animals' social environment. We also discuss potential caveats related to their use.     

A social network analysis of primate groups

Primate social systems are difficult to characterize, and existing classification schemes have been criticized for being overly simplifying, formulated only on a verbal level or partly inconsistent. Social network analysis comprises a collection of analytical tools rooted in the framework of graph theory that were developed to study human social interaction patterns. More recently these techniques have been successfully applied to examine animal societies. Primate social systems differ from those of humans in both size and density, requiring an approach that puts more emphasis on the quality of relationships. Here, we discuss a set of network measures that are useful to describe primate social organization and we present the results of a network analysis of 70 groups from 30 different species. For this purpose we concentrated on structural measures on the group level, describing the distribution of interaction patterns, centrality, and group structuring. We found considerable variability in those measures, reflecting the high degree of diversity of primate social organizations. By characterizing primate groups in terms of their network metrics we can draw a much finer picture of their internal structure that might be useful for species comparisons as well as the interpretation of social behavior.     

Hormones,behavior, and social network analysis: Exploring associations between cortisol,testosterone, and network structure

We used a new interdisciplinary paradigm of social network analysis (SNA) to investigate associations between hormones and social network structures. We examine these biobehavioral processes and test hypotheses about how hormones are associated with social network structures using exponential random graph modeling (ERGM) in a cohort of first-year students (n = 74; 93% female; M age = 27 years) from a highly competitive, accelerated nursing program. Participants completed friendship nominations and as a group simultaneously donated saliva (later assayed for cortisol and testosterone). ERGM analyses revealed that salivary cortisol levels were inversely associated with the number of outgoing ties (i.e., network activity). By contrast, testosterone was not related to friendship network structure. Integration of SNA and salivary bioscience creates a novel approach to understanding hormone–behavior relationships within the context of human social ecologies.     

Formidable females redux:male social integration into female networks and the value of dynamic multilayer networks

The development of multilayer network techniques is a boon for researchers who wish to understand how different interaction layers might influence each other,and how these in turn might influence group dynamics.Here,we investigate how integration between male and female grooming and aggression interaction networks influences male power trajectories in vervet monkeys Chlorocebus pygerythrus.Our previous analyses of this phenomenon used a monolayer approach,and our aim here is to extend these analyses using a dynamic multilayer approach.To do so,we constructed a temporal series of male and female interaction layers.We then used a multivariate multilevel autoregression model to compare cross-lagged associations between a male's centrality in the female grooming layer and changes in male Elo ratings.Our results confirmed our original findings:changes in male centrality within the female grooming network were weakly but positively tied to changes in their Elo ratings.However,the multilayer network approach offered additional insights into this social process,identifying how changes in a male's centrality cascade through the other network layers.This dynamic view indicates that the changes in Elo ratings are likely to be short-lived,but that male centrality within the female network had a much stronger impact throughout the multilayer network as a whole,especially on reducing intermale aggression(i.e.,aggression directed by males toward other males).We suggest that multilayer social network approaches can take advantage of increased amounts of social data that are more commonly collected these days,using a variety of methods.Such data are inherently multilevel and multilayered,and thus offer the ability to quantify more precisely the dynamics of animal social behaviors.     

Social traits,social networks and evolutionary biology

The social environment is both an important agent of selection for most organisms, and an emergent property of their interactions. As an aggregation of interactions among members of a population, the social environment is a product of many sets of relationships and so can be represented as a network or matrix. Social network analysis in animals has focused on why these networks possess the structure they do, and whether individuals’ network traits, representing some aspect of their social phenotype, relate to their fitness. Meanwhile, quantitative geneticists have demonstrated that traits expressed in a social context can depend on the phenotypes and genotypes of interacting partners, leading to influences of the social environment on the traits and fitness of individuals and the evolutionary trajectories of populations. Therefore, both fields are investigating similar topics, yet have arrived at these points relatively independently. We review how these approaches are diverged, and yet how they retain clear parallelism and so strong potential for complementarity. This demonstrates that, despite separate bodies of theory, advances in one might inform the other. Techniques in network analysis for quantifying social phenotypes, and for identifying community structure, should be useful for those studying the relationship between individual behaviour and group‐level phenotypes. Entering social association matrices into quantitative genetic models may also reduce bias in heritability estimates, and allow the estimation of the influence of social connectedness on trait expression. Current methods for measuring natural selection in a social context explicitly account for the fact that a trait is not necessarily the property of a single individual, something the network approaches have not yet considered when relating network metrics to individual fitness. Harnessing evolutionary models that consider traits affected by genes in other individuals (i.e. indirect genetic effects) provides the potential to understand how entire networks of social interactions in populations influence phenotypes and predict how these traits may evolve. By theoretical integration of social network analysis and quantitative genetics, we hope to identify areas of compatibility and incompatibility and to direct research efforts towards the most promising areas. Continuing this synthesis could provide important insights into the evolution of traits expressed in a social context and the evolutionary consequences of complex and nuanced social phenotypes.     

An example implementation of STEM in preschool education: Carrying eggs without breaking

Abstract

Although there are many examples of activities for using STEM in the preschool education, the number of studies on how these activities can be used in practice, how children can react, and what kind of products they can design are limited. The aim of this study is to implement the STEM activity called “How do I carry the eggs without breaking?” which is planned for 48-66-month-old children and to convey the implementation stages in order to set an example for other researchers. Because there is no any guidelines or models about implementation of STEM activities, we set out 7 steps from STEM-oriented studies, which are shown in detail in the study, have been formed by focusing on the points that should be at an STEM activity. During all the steps of the activity, students cooperatively worked in groups and their social relations were improved. While children were trying to test their designs by carrying real eggs, they had fun because in the designation process they used ping-pong balls. At the testing stage, some designs of the groups could not have carried the eggs and they were broken. The teacher had guided them to test the new ideas on redesign.     

A Principal Component Analysis of 39 Scientific Impact Measures

Background

The impact of scientific publications has traditionally been expressed in terms of citation counts. However, scientific activity has moved online over the past decade. To better capture scientific impact in the digital era, a variety of new impact measures has been proposed on the basis of social network analysis and usage log data. Here we investigate how these new measures relate to each other, and how accurately and completely they express scientific impact.

Methodology

We performed a principal component analysis of the rankings produced by 39 existing and proposed measures of scholarly impact that were calculated on the basis of both citation and usage log data.

Conclusions

Our results indicate that the notion of scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator, although some measures are more suitable than others. The commonly used citation Impact Factor is not positioned at the core of this construct, but at its periphery, and should thus be used with caution.     

Implementation of chemometrics,design of experiments,and neural network analysis for prior process knowledge assessment,failure modes and effect analysis,scale-down model development,and process characterization for a chromatographic purification of Teriparatide

Process understanding and characterization forms the foundation, ensuring consistent and robust biologics manufacturing process. Using appropriate modeling tools and machine learning approaches, the process data can be monitored in real time to avoid manufacturing risks. In this article, we have outlined an approach toward implementation of chemometrics and machine learning tools (neural network analysis) to model and predict the behavior of a mixed-mode chromatography step for a biosimilar (Teriparatide) as a case study. The process development data and process knowledge was assimilated into a prior process knowledge assessment using chemometrics tools to derive important parameters critical to performance indicators (i.e., potential quality and process attributes) and to establish the severity ranking for the FMEA analysis. The characterization data of the chromatographic operation are presented alongwith the determination of the critical, key and non- key process parameters, set points, operating, process acceptance and characterized ranges. The scale-down model establishment was assessed using traditional approaches and novel approaches like batch evolution model and neural network analysis. The batch evolution model was further used to demonstrate batch monitoring through direct chromatographic data, thus demonstrating its application for continuos process verification. Assimilation of process knowledge through a structured data acquisition approach, built-in from process development to continuous process verification was demonstrated to result in a data analytics driven model that can be coupled with machine learning tools for real time process monitoring. We recommend application of these approaches with the FDA guidance on stage wise process development and validation to reduce manufacturing risks.     

Ants and agents: a process algebra approach to modelling ant colony behaviour

Process algebras are widely used in the analysis of distributed computer systems. They allow formal reasoning about how the various components of a system contribute to its overall behaviour. In this paper we show how process algebras can be usefully applied to understanding social insect biology, in particular to studying the relationship between algorithmic behaviour of individual insects and the dynamical behaviour of their colony. We argue that process algebras provide a useful formalism for understanding this relationship, since they combine computer simulation, Markov chain analysis and mean-field methods of analysis. Indeed, process algebras can provide a framework for relating these three methods of analysis to each other and to experiments. We illustrate our approach with a series of graded examples of modelling activity in ant colonies.     

Towards a Methodology for Validation of Centrality Measures in Complex Networks

Background

Living systems are associated with Social networks — networks made up of nodes, some of which may be more important in various aspects as compared to others. While different quantitative measures labeled as “centralities” have previously been used in the network analysis community to find out influential nodes in a network, it is debatable how valid the centrality measures actually are. In other words, the research question that remains unanswered is: how exactly do these measures perform in the real world? So, as an example, if a centrality of a particular node identifies it to be important, is the node actually important?

Purpose

The goal of this paper is not just to perform a traditional social network analysis but rather to evaluate different centrality measures by conducting an empirical study analyzing exactly how do network centralities correlate with data from published multidisciplinary network data sets.

Method

We take standard published network data sets while using a random network to establish a baseline. These data sets included the Zachary''s Karate Club network, dolphin social network and a neural network of nematode Caenorhabditis elegans. Each of the data sets was analyzed in terms of different centrality measures and compared with existing knowledge from associated published articles to review the role of each centrality measure in the determination of influential nodes.

Results

Our empirical analysis demonstrates that in the chosen network data sets, nodes which had a high Closeness Centrality also had a high Eccentricity Centrality. Likewise high Degree Centrality also correlated closely with a high Eigenvector Centrality. Whereas Betweenness Centrality varied according to network topology and did not demonstrate any noticeable pattern. In terms of identification of key nodes, we discovered that as compared with other centrality measures, Eigenvector and Eccentricity Centralities were better able to identify important nodes.     

Ecological validity of social interaction tests in rats and mice

Different rat and mouse models are used in studies of social interactions. Simple behavioral measures, which are commonly used in the laboratory, allow to perform relatively short experiments and to use multiple brain manipulation techniques. However, too much focus on the simplest behavioral models generates a serious risk of reducing ecological validity or even studying phenomena which would never happen outside of the laboratory. In this review, we discuss the suitability of mice and rats as model organisms for studying social behaviors, with focus on social transmission of fear paradigms. First, we briefly introduce the concept of domestication and what impact it had on laboratory rodents. Then, we present two aspects of social behaviors, sociability and dominance, which are crucial for social organization in these species. Finally, we present experimental models used for studying how animals transmit information about danger between each other, and how these models may reflect what happens in the natural environment. We discuss the difficulties that arise from our limited knowledge of rat and mouse ecology, especially their social life. We also explore the subject of balancing ecological validity and controllability in rodent models of social behaviors, the latter being particularly important for studying brain activity. Although it is very challenging, an efficient program for social neuroscience research should, in our opinion, aim at bridging the gap between laboratory and field studies.     

Finding Influential Spreaders from Human Activity beyond Network Location

Most centralities proposed for identifying influential spreaders on social networks to either spread a message or to stop an epidemic require the full topological information of the network on which spreading occurs. In practice, however, collecting all connections between agents in social networks can be hardly achieved. As a result, such metrics could be difficult to apply to real social networks. Consequently, a new approach for identifying influential people without the explicit network information is demanded in order to provide an efficient immunization or spreading strategy, in a practical sense. In this study, we seek a possible way for finding influential spreaders by using the social mechanisms of how social connections are formed in real networks. We find that a reliable immunization scheme can be achieved by asking people how they interact with each other. From these surveys we find that the probabilistic tendency to connect to a hub has the strongest predictive power for influential spreaders among tested social mechanisms. Our observation also suggests that people who connect different communities is more likely to be an influential spreader when a network has a strong modular structure. Our finding implies that not only the effect of network location but also the behavior of individuals is important to design optimal immunization or spreading schemes.     

Large-scale properties of clustered networks: implications for disease dynamics

We consider previously proposed procedures for generating clustered networks and investigate how these procedures lead to differences in network properties other than clustering. We interpret our findings in terms of the effect of the network structure on the disease outbreak threshold and disease dynamics. To generate null-model networks for comparison, we implement an assortativity-conserving rewiring algorithm that alters the level of clustering while causing minimal impact on other properties. We show that many theoretical network models used to generate networks with a particular property often lead to significant changes in network properties other than that of interest. For high levels of clustering, different procedures lead to networks that differ in degree heterogeneity and assortativity, and in broader scale measures such as ?(0) and the distribution of shortest path lengths. Hence, care must be taken when investigating the implications of network properties for disease transmission or other dynamic process that the network supports.     

A novel DNAseq program for enhanced analysis of Illumina GAII data: a case study on antibody complementarity-determining regions

High-throughput DNA sequencing technologies are increasingly becoming powerful systems for the comprehensive analysis of variations in whole genomes or various DNA libraries. As they are capable of producing massive collections of short sequences with varying lengths, a major challenge is how to turn these reads into biologically meaningful information. The first stage is to assemble the short reads into longer sequences through an in silico process. However, currently available software/programs allow only the assembly of abundant sequences, which apparently results in the loss of highly variable (or rare) sequences or creates artefact assemblies. In this paper, we describe a novel program (DNAseq) that is capable of assembling highly variable sequences and displaying them directly for phylogenetic analysis. In addition, this program is Microsoft Windows-based and runs by a normal PC with 700MB RAM for a general use. We have applied it to analyse a human naive single-chain antibody (scFv) library, comprehensively revealing the diversity of antibody variable complementarity-determining regions (CDRs) and their families. Although only a scFv library was exemplified here, we envisage that this program could be applicable to other genome libraries.     

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

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