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

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.

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Identification of cohesive subgroups in a university hall of residence during the COVID-19 pandemic using a social network analysis approach

Scientific Reports ◽

10.1038/s41598-021-01390-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Pilar Marqués-Sánchez ◽

Arrate Pinto-Carral ◽

Tania Fernández-Villa ◽

Ana Vázquez-Casares ◽

Cristina Liébana-Presa ◽

...

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

University Students ◽

Global Network ◽

Case Study Methodology ◽

And Gender ◽

The One ◽

Relationship Structure ◽

The University

AbstractThe aims: (i) analyze connectivity between subgroups of university students, (ii) assess which bridges of relational contacts are essential for connecting or disconnecting subgroups and (iii) to explore the similarities between the attributes of the subgroup nodes in relation to the pandemic context. During the COVID-19 pandemic, young university students have experienced significant changes in their relationships, especially in the halls of residence. Previous research has shown the importance of relationship structure in contagion processes. However, there is a lack of studies in the university setting, where students live closely together. The case study methodology was applied to carry out a descriptive study. The participation consisted of 43 university students living in the same hall of residence. Social network analysis has been applied for data analysis. Factions and Girvan–Newman algorithms have been applied to detect the existing cohesive subgroups. The UCINET tool was used for the calculation of the SNA measure. A visualization of the global network will be carried out using Gephi software. After applying the Girvan–Newman and Factions, in both cases it was found that the best division into subgroups was the one that divided the network into 4 subgroups. There is high degree of cohesion within the subgroups and a low cohesion between them. The relationship between subgroup membership and gender was significant. The degree of COVID-19 infection is related to the degree of clustering between the students. College students form subgroups in their residence. Social network analysis facilitates an understanding of structural behavior during the pandemic. The study provides evidence on the importance of gender, race and the building where they live in creating network structures that favor, or not, contagion during a pandemic.

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Foreign Capital and Economic Growth: A Social Network Analysis, 2001–2017

Sociological Perspectives ◽

10.1177/07311214211035150 ◽

2021 ◽

pp. 073112142110351

Author(s):

Rob Clark ◽

Jeffrey Kentor

Keyword(s):

Economic Growth ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Direct Investment ◽

Fixed Effects ◽

Foreign Capital ◽

Global Network ◽

Data Sets ◽

The Impact

Foreign direct investment (FDI) holds a substantial and rapidly growing presence across every region of the world. However, our understanding of how foreign capital impacts economic growth in receiving and investing countries remains in question, despite nearly five decades of research. Our study contributes to this long-standing debate by (1) applying social network analysis to the FDI-growth literature, (2) utilizing recently available bilateral data for a global sample of countries during the post-2000 period, and (3) examining the impact of both inward and outward foreign capital on economic growth. While conventional measures of FDI typically focus on investment volume, we argue that the network structure of investment relations may be equally—or more—important. We construct a global network of FDI during the 2001–2017 period, bringing together two data sets: (1) the United Nations Conference on Trade and Development’s Bilateral FDI Statistics, and (2) the International Monetary Fund’s Coordinated Direct Investment Survey. We then calculate network centrality scores that reflect each country’s level of inward and outward embeddedness in the global FDI network. Drawing from a sample of 1,467 observations across 137 countries during the 2001–2017 period, we estimate two-way fixed effects models to examine the effect of FDI centrality on economic growth. Net of other predictors, we find that inward and outward centrality are positively—and independently—associated with growth, while more conventional measures of foreign capital display weaker and inconsistent effects.

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Personality in the context of social networks

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2010.0216 ◽

2010 ◽

Vol 365 (1560) ◽

pp. 4099-4106 ◽

Cited By ~ 126

Author(s):

J. Krause ◽

R. James ◽

D. P. Croft

Keyword(s):

Fine Structure ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Disease Transmission ◽

Dyadic Relationships ◽

Network Position ◽

Animal Populations ◽

The Social ◽

Interactive Strategies

There is great interest in environmental effects on the development and evolution of animal personality traits. An important component of an individual's environment is its social environment. However, few studies look beyond dyadic relationships and try to place the personality of individuals in the context of a social network. Social network analysis provides us with many new metrics to characterize the social fine-structure of populations and, therefore, with an opportunity to gain an understanding of the role that different personalities play in groups, communities and populations regarding information or disease transmission or in terms of cooperation and policing of social conflicts. The network position of an individual is largely a consequence of its interactive strategies. However, the network position can also shape an individual's experiences (especially in the case of juveniles) and therefore can influence the way in which it interacts with others in future. Finally, over evolutionary time, the social fine-structure of animal populations (as quantified by social network analysis) can have important consequences for the evolution of personalities—an approach that goes beyond the conventional game-theoretic analyses that assumed random mixing of individuals in populations.

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A Social Network Analysis of Social Cohesion in a Constructed Pride: Implications for Ex Situ Reintroduction of the African Lion (Panthera leo)

PLoS ONE ◽

10.1371/journal.pone.0082541 ◽

2013 ◽

Vol 8 (12) ◽

pp. e82541 ◽

Cited By ~ 8

Author(s):

Jackie Abell ◽

Morgan W. B. Kirzinger ◽

Yvonne Gordon ◽

Jacqui Kirk ◽

Rae Kokeŝ ◽

...

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Social Cohesion ◽

Ex Situ ◽

Panthera Leo ◽

African Lion

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

Frontiers in Genetics ◽

10.3389/fgene.2020.611073 ◽

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.

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Spatial Association and Effect Evaluation of CO2 Emission in the Chengdu-Chongqing Urban Agglomeration: Quantitative Evidence from Social Network Analysis

Sustainability ◽

10.3390/su11010001 ◽

2018 ◽

Vol 11 (1) ◽

pp. 1 ◽

Cited By ~ 34

Author(s):

Jinzhao Song ◽

Qing Feng ◽

Xiaoping Wang ◽

Hanliang Fu ◽

Wei Jiang ◽

...

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Spatial Structure ◽

Emission Intensity ◽

Co2 Emission ◽

Spatial Association ◽

Urban Agglomeration ◽

Global Network ◽

Quantitative Evidence

Urban agglomeration, an established urban spatial pattern, contributes to the spatial association and dependence of city-level CO2 emission distribution while boosting regional economic growth. Exploring this spatial association and dependence is conducive to the implementation of effective and coordinated policies for regional level CO2 reduction. This study calculated CO2 emissions from 2005–2016 in the Chengdu-Chongqing urban agglomeration with the IPAT model, and empirically explored the spatial structure pattern and association effect of CO2 across the area leveraged by the social network analysis. The findings revealed the following: (1) The spatial structure of CO2 emission in the area is a complex network pattern, and in the sample period, the CO2 emission association relations increased steadily and the network stabilization remains strengthened; (2) the centrality of the cities in this area can be categorized into three classes: Chengdu and Chongqing are defined as the first class, the second class covers Deyang, Mianyang, Yibin, and Nanchong, and the third class includes Zigong, Suining, Meishan, and Guangan—the number of cities in this class is on the rise; (3) the network is divided into four subgroups: the area around Chengdu, south Sichuan, northeast Sichuan, and west Chongqing where the spillover effect of CO2 is greatest; and (4) the higher density of the global network of CO2 emission considerably reduces regional emission intensity and narrows the differences among regions. Individual networks with higher centrality are also found to have lower emission intensity.

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An overview of Software Applications for Social Network Analysis

International Review of Social Research ◽

10.1515/irsr-2013-0023 ◽

2013 ◽

Vol 3 (3) ◽

pp. 71-77 ◽

Cited By ~ 6

Author(s):

Ioana-Alexandra Apostolato

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Probabilistic Models ◽

Software Applications ◽

Software Packages ◽

Node Removal ◽

Analytical Tools ◽

Analysis Of Social Networks ◽

Text Mining Tool

Abstract There is a great variety of software tools that has been developed within the last 20 years, as to facilitate and support the qualitative and quantitative analysis of social networks. This paper gives a brief overview of some of the most popular software packages for social network analysis: Pajek, UCINET 6, NetDraw, Gephi, E-Net, KeyPlayer 1, StOCNET and Automap. Pajek has efficient algorithms for the analysis of large networks, while UCINET 6 includes multiple analytical tools highly efficient for exploring and measuring social network structures. NetDraw, nested in UCINET 6, and Gephi allow network visualization. E-Net and KeyPlayer 1 satisfy rather specific and well-oriented purposes: ego-network analysis and network key-player operations (node removal or utilization). StOCNET provides a platform for statistical methods focusing on probabilistic models, while Automap is a text mining tool for analyzing text relational data.

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