scholarly journals Prospects for the Analysis and Reduction of Damaging Behaviour in Group-Housed Livestock, With Application to Pig Breeding

2020 ◽  
Vol 11 ◽  
Author(s):  
Laurianne Canario ◽  
Piter Bijma ◽  
Ingrid David ◽  
Irene Camerlink ◽  
Alexandre Martin ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Missing Data ◽  
Network Analysis ◽  
Social Interactions ◽  
Emotional Distress ◽  
Social Groups ◽  
Genetic Selection ◽  
Accurate Estimation ◽  
Capture Recapture

Innovations in the breeding and management of pigs are needed to improve the performance and welfare of animals raised in social groups, and in particular to minimise biting and damage to group mates. Depending on the context, social interactions between pigs can be frequent or infrequent, aggressive, or non-aggressive. Injuries or emotional distress may follow. The behaviours leading to damage to conspecifics include progeny savaging, tail, ear or vulva biting, and excessive aggression. In combination with changes in husbandry practices designed to improve living conditions, refined methods of genetic selection may be a solution reducing these behaviours. Knowledge gaps relating to lack of data and limits in statistical analyses have been identified. The originality of this paper lies in its proposal of several statistical methods for common use in analysing and predicting unwanted behaviours, and for genetic use in the breeding context. We focus on models of interaction reflecting the identity and behaviour of group mates which can be applied directly to damaging traits, social network analysis to define new and more integrative traits, and capture-recapture analysis to replace missing data by estimating the probability of behaviours. We provide the rationale for each method and suggest they should be combined for a more accurate estimation of the variation underlying damaging behaviours.

Heterophily in social groups formation: a social network analysis

2018 ◽  
Vol 53 (2) ◽  
pp. 599-619 ◽  
Author(s):  
Oriol Barranco ◽  
Carlos Lozares ◽  
Dafne Muntanyola-Saura
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Groups

scholarly journals Learning and robustness to catch-and-release fishing in a shark social network

2017 ◽  
Vol 13 (3) ◽  
pp. 20160824 ◽  
Author(s):  
Johann Mourier ◽  
Culum Brown ◽  
Serge Planes
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Cohesion ◽  
Network Robustness ◽  
Global Network ◽  
Catch And Release ◽  
Animal Populations ◽  
Capture Recapture ◽  
Node Removal

Individuals can play different roles in maintaining connectivity and social cohesion in animal populations and thereby influence population robustness to perturbations. We performed a social network analysis in a reef shark population to assess the vulnerability of the global network to node removal under different scenarios. We found that the network was generally robust to the removal of nodes with high centrality. The network appeared also highly robust to experimental fishing. Individual shark catchability decreased as a function of experience, as revealed by comparing capture frequency and site presence. Altogether, these features suggest that individuals learnt to avoid capture, which ultimately increased network robustness to experimental catch-and-release. Our results also suggest that some caution must be taken when using capture–recapture models often used to assess population size as assumptions (such as equal probabilities of capture and recapture) may be violated by individual learning to escape recapture.

Social network analysis of small social groups: Application of a hurdle GLMM approach in the Alpine marmot ( Marmota marmota )

Ethology ◽  
2021 ◽  
Author(s):  
Matteo Panaccio ◽  
Caterina Ferrari ◽  
Bruno Bassano ◽  
Christina R. Stanley ◽  
Achaz von Hardenberg
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Groups ◽  
Marmota Marmota ◽  
Alpine Marmot

An Introduction to Social Network Analysis for Personality and Social Psychologists

2017 ◽  
Vol 8 (4) ◽  
pp. 442-453 ◽  
Author(s):  
Allan Clifton ◽  
Gregory D. Webster
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Groups ◽  
Network Data ◽  
Personality Psychology ◽  
Social Network Data ◽  
And Behavior

Social network analysis (SNA) is a methodology for studying the connections and behavior of individuals within social groups. Despite its relevance to social and personality psychology, SNA has been underutilized in these fields. We first examine the paucity of SNA research in social and personality journals. Next we describe methodological decisions that must be made before collecting social network data, with benefits and drawbacks for each. We discuss common SNAs and give an overview of software available for SNA. We provide examples from the literature of SNA for both one-mode and two-mode network data. Finally, we make recommendations to researchers considering incorporating SNA into their research.

scholarly journals Understanding Classrooms through Social Network Analysis: A Primer for Social Network Analysis in Education Research

2014 ◽  
Vol 13 (2) ◽  
pp. 167-178 ◽  
Author(s):  
Daniel Z. Grunspan ◽  
Benjamin L. Wiggins ◽  
Steven M. Goodreau
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Interactions ◽  
Human Subjects ◽  
Relational Data ◽  
Undergraduate Biology ◽  
Collection Data ◽  
Generative Processes ◽  
Network Studies

Social interactions between students are a major and underexplored part of undergraduate education. Understanding how learning relationships form in undergraduate classrooms, as well as the impacts these relationships have on learning outcomes, can inform educators in unique ways and improve educational reform. Social network analysis (SNA) provides the necessary tool kit for investigating questions involving relational data. We introduce basic concepts in SNA, along with methods for data collection, data processing, and data analysis, using a previously collected example study on an undergraduate biology classroom as a tutorial. We conduct descriptive analyses of the structure of the network of costudying relationships. We explore generative processes that create observed study networks between students and also test for an association between network position and success on exams. We also cover practical issues, such as the unique aspects of human subjects review for network studies. Our aims are to convince readers that using SNA in classroom environments allows rich and informative analyses to take place and to provide some initial tools for doing so, in the process inspiring future educational studies incorporating relational data.

scholarly journals El análisis de redes sociales como instrumento del Trabajo Social frente a la COVID 19

2021 ◽  
pp. 35
Author(s):  
Joaquín Castillo de Mesa
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Interactions ◽  
Redes Sociales ◽  
Infected People ◽  
Social Nature ◽  
The Social ◽  
Community Transmission ◽  
Trabajo Social

El consenso científico señala que la mejor manera de frenar la propagación de la COVID-19 es mediante el rastreo de los contactos de las personas infectadas. Esta medida tiene carácter social, ya que busca analizar las redes de las personas para detectar de forma temprana quiénes están en riesgo de haber sido infectados, alertarles e imponerles la cuarentena, una medida de aislamiento social que impida la potencial propagación.  Hasta el momento mucho se ha hablado sobre qué profesionales deben realizar este rastreo pero poco acerca de cómo se debe realizar este rastreo. En este artículo, en primer lugar, se define qué es el rastreo, qué profesionales están más preparados para estas tareas y cómo se está llevando a cabo en España, encontrando ciertas carencias en cuanto al uso de metodologías científicas que apoyen la labor de rastreo. A partir de la literatura científica que analiza cómo afecta la socialización a la propagación se desarrolla una simulación sobre cómo se puede propagar la COVID 19 durante las interacciones sociales de las personas en sus distintos ámbitos de socialización. Sobre esta simulación se utiliza análisis de redes sociales y determinados algoritmos de detección de comunidades y de análisis de cohesión, para mostrar la idoneidad de estas metodologías para que el rastreo. Los resultados muestran que con el apoyo del análisis de redes sociales y de determinados algoritmos se accede de forma precoz a información clave sobre comunidades formadas en la estructura de red y sobre quiénes son los superpropagadores y los intermediadores entre las comunidades detectadas. Esto puede ayudar a priorizar la puesta en contacto con estas personas para cortar las cadenas de trasmisión comunitaria. Finalmente discutimos acerca de la idoneidad de que los profesionales del Trabajo Social se capaciten en estas metodologías para poder desarrollar esta labor del rastreo.Scientific consensus indicates that the best way to slow the spread of COVID 19 is by tracing the contacts of infected people. This measure has a social nature, since it seeks to analyze people’s networks to detect early who is at risk of being infected, alert them and impose quarantine, a measure of social isolation that prevents the potential spread. So far, much has been said about which professionals should perform this screening but Little about how it should be done. In this article, in the first place, it is defined what tracking is, which professionals are best prepared for the use of scientific methodologies that support tracking word. From the scientific literature that analyzes how socialization affects the spread, a simulation is developed on how COVID 19 can spread during the social interactions of people in their different areas of socialization. On this simulation, social network analysis and certain algorithms for community detection and cohesion analysis are used to show the suitability of these methodologies for tracking. The results show that with the support of social network analysis and certain algorithms, key information about communities formed in the network structure and who are the super-propagators and intermediaries between the detected communities is accessed early. This can help prioritize contacting these people to cut the chains of community transmission. Finally, we discuss the suitability for Social Work professionals to be trained in these methodologies in order to develop this tracking work.

Multiscalar Perspectives on Social Networks in the Late Prehispanic Southwest

2015 ◽  
Vol 80 (1) ◽  
pp. 3-24 ◽  
Author(s):  
Barbara J. Mills ◽  
Matthew A. Peeples ◽  
W. Randall Haas ◽  
Lewis Borck ◽  
Jeffery J. Clark ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Interactions ◽  
Spatial Scales ◽  
Social Processes ◽  
Religious Movements ◽  
Archaeological Data ◽  
Social Landscape ◽  
Over Time

AbstractAnalyzing historical trajectories of social interactions at varying scales can lead to complementary interpretations of relationships among archaeological settlements. We use social network analysis combined with geographic information systems at three spatial scales over time in the western U.S. Southwest to show how the same social processes affected network dynamics at each scale. The period we address, A.D. 1200–1450, was characterized by migration and demographic upheaval. The tumultuous late thirteenth-century interval was followed by population coalescence and the development of widespread religious movements in the fourteenth and fifteenth centuries. In the southern Southwest these processes resulted in a highly connected network that drew in members of different settlements within and between different valleys that had previously been distinct. In the northern Southwest networks were initially highly connected followed by a more fragmented social landscape. We examine how different network textures emerged at each scale through 50-year snapshots. The results demonstrate the usefulness of applying a multiscalar approach to complex historical trajectories and the potential for social network analysis as applied to archaeological data.

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