Animal social network inference and permutations for ecologists in R using asnipe
01 Dec 2013-Methods in Ecology and Evolution (John Wiley and Sons Inc.)-Vol. 4, Iss: 12, pp 1187-1194
TL;DR: A novel approach to estimating re‐association rates of time between frequently sampled individuals is included, which bridges a gap in the tools that are available to biologists wishing to analyse animal social networks in R.
Abstract: Summary
The sampling of animals for the purpose of measuring associations and interactions between individuals has led to the development of several statistical methods to deal with biases inherent in these data. However, these methods are typically computationally intensive and complex to implement.
Here, I provide a software package that supports a range of these analyses in the R statistical computing environment. This package includes a novel approach to estimating re-association rates of time between frequently sampled individuals.
I include extended demonstration of the syntax and examples of the ability for this software to interface with existing network analysis packages in R.
This bridges a gap in the tools that are available to biologists wishing to analyse animal social networks in R.
Citations
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TL;DR: The under‐exploited potential of experimental manipulations on social networks to address research questions is highlighted, and an overview of methods for quantifying properties of nodes and networks, as well as for testing hypotheses concerning network structure and network processes are provided.
Abstract: Summary
Animal social networks are descriptions of social structure which, aside from their intrinsic interest for understanding sociality, can have significant bearing across many fields of biology.
Network analysis provides a flexible toolbox for testing a broad range of hypotheses, and for describing the social system of species or populations in a quantitative and comparable manner. However, it requires careful consideration of underlying assumptions, in particular differentiating real from observed networks and controlling for inherent biases that are common in social data.
We provide a practical guide for using this framework to analyse animal social systems and test hypotheses. First, we discuss key considerations when defining nodes and edges, and when designing methods for collecting data. We discuss different approaches for inferring social networks from these data and displaying them. We then provide an overview of methods for quantifying properties of nodes and networks, as well as for testing hypotheses concerning network structure and network processes. Finally, we provide information about assessing the power and accuracy of an observed network.
Alongside this manuscript, we provide appendices containing background information on common programming routines and worked examples of how to perform network analysis using the r programming language.
We conclude by discussing some of the major current challenges in social network analysis and interesting future directions. In particular, we highlight the under-exploited potential of experimental manipulations on social networks to address research questions.
648 citations
Cites background from "Animal social network inference and..."
...This involves keeping different aspects of the data constant in each model to identify whether they affect social structure (Farine 2013b)....
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TL;DR: In providing the first experimental demonstration of conformity in a wild non-primate, and of cultural norms in foraging techniques in any wild animal, the results suggest a much broader taxonomic occurrence of such an apparently complex cultural behaviour.
Abstract: In human societies, cultural norms arise when behaviours are transmitted through social networks via high-fidelity social learning. However, a paucity of experimental studies has meant that there is no comparable understanding of the process by which socially transmitted behaviours might spread and persist in animal populations. Here we show experimental evidence of the establishment of foraging traditions in a wild bird population. We introduced alternative novel foraging techniques into replicated wild sub-populations of great tits (Parus major) and used automated tracking to map the diffusion, establishment and long-term persistence of the seeded innovations. Furthermore, we used social network analysis to examine the social factors that influenced diffusion dynamics. From only two trained birds in each sub-population, the information spread rapidly through social network ties, to reach an average of 75% of individuals, with a total of 414 knowledgeable individuals performing 57,909 solutions over all replicates. The sub-populations were heavily biased towards using the technique that was originally introduced, resulting in established local traditions that were stable over two generations, despite a high population turnover. Finally, we demonstrate a strong effect of social conformity, with individuals disproportionately adopting the most frequent local variant when first acquiring an innovation, and continuing to favour social information over personal information. Cultural conformity is thought to be a key factor in the evolution of complex culture in humans. In providing the first experimental demonstration of conformity in a wild non-primate, and of cultural norms in foraging techniques in any wild animal, our results suggest a much broader taxonomic occurrence of such an apparently complex cultural behaviour.
541 citations
TL;DR: It is shown that permutations of the raw observational (or ‘pre‐network’) data consistently account for underlying structure in the generated social network, and thus can reduce both type I and type II error rates.
Abstract: Null models are an important component of the social network analysis toolbox. However, their use in hypothesis testing is still not widespread. Furthermore, several different approaches for constructing null models exist, each with their relative strengths and weaknesses, and often testing different hypotheses.In this study, I highlight why null models are important for robust hypothesis testing in studies of animal social networks. Using simulated data containing a known observation bias, I test how different statistical tests and null models perform if such a bias was unknown.I show that permutations of the raw observational (or 'pre-network') data consistently account for underlying structure in the generated social network, and thus can reduce both type I and type II error rates. However, permutations of pre-network data remain relatively uncommon in animal social network analysis because they are challenging to implement for certain data types, particularly those from focal follows and GPS tracking.I explain simple routines that can easily be implemented across different types of data, and supply R code that applies each type of null model to the same simulated dataset. The R code can easily be modified to test hypotheses with empirical data. Widespread use of pre-network data permutation methods will benefit researchers by facilitating robust hypothesis testing.
312 citations
Cites background or methods from "Animal social network inference and..."
...For this paper (and elsewhere, e.g. Farine 2013; Boogert, Farine & Spencer 2014; Farine & Whitehead 2015), I have found that linear or mixedmodels are useful for extracting test statistics when comparing among nodes in a network....
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...I have implement a function inmy R package ASNIPE (Farine 2013) to enable customised null models to be used in conjunction with a QAP or MRQAP regression....
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TL;DR: This paper extends existing approaches that calculate the assortativity coefficient of both nominal classes and continuous traits to incorporate weighted associations and uses simulated networks to show that weighted assortment coefficients are more robust than those calculated on binary networks to added noise that could arise from random interactions or sampling errors.
189 citations
TL;DR: It is suggested that network theory can be used to model and predict the influence of ecological and environmental parameters on animal movement, focusing on spatial and social connectivity, with fundamental implications for conservation.
Abstract: New technologies have vastly increased the available data on animal movement and behaviour. Consequently, new methods deciphering the spatial and temporal interactions between individuals and their environments are vital. Network analyses offer a powerful suite of tools to disentangle the complexity within these dynamic systems, and we review these tools, their application, and how they have generated new ecological and behavioural insights. We suggest that network theory can be used to model and predict the influence of ecological and environmental parameters on animal movement, focusing on spatial and social connectivity, with fundamental implications for conservation. Refining how we construct and randomise spatial networks at different temporal scales will help to establish network theory as a prominent, hypothesis-generating tool in movement ecology.
159 citations
References
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Journal Article•
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Abstract: Copyright (©) 1999–2012 R Foundation for Statistical Computing. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the R Core Team.
272,030 citations
Book•
23 Jul 2020
TL;DR: The idea of a randomization test has been explored in the context of data analysis for a long time as mentioned in this paper, and it has been applied in a variety of applications in biology, such as single species ecology and community ecology.
Abstract: Preface to the Second Edition Preface to the First Edition Randomization The Idea of a Randomization Test Examples of Randomization Tests Aspects of Randomization Testing Raised by the Examples Sampling the Randomization Distribution or Systematic Enumeration Equivalent Test Statistics Significance Levels for Classical and Randomization Tests Limitations of Randomization Tests Confidence Limits by Randomization Applications of Randomization in Biology Single Species Ecology Genetics, Evolution and Natural Selection Community Ecology Randomization and Observational Studies Chapter Summary The Jackknife The Jackknife Estimator Applications of Jackknifing in Biology Single Species Analyses Genetics, Evolution and Natural Selection Community Ecology Chapter Summary The Bootstrap Resampling with Replacement Standard Bootstrap Confidence Limits Simple Percentile Confidence Limits Bias Corrected Percentile Confidence Limits Accelerated Bias Corrected Percentile Limits Other Methods for Constructing Confidence Intervals Transformations to Improve Bootstrap Intervals Parametric Confidence Intervals A Better Estimate of Bias Bootstrap Tests of Significance Balanced Bootstrap Sampling Applications of Bootstrapping in Biology Single Species Ecology Genetics, Evolution and Natural Selection Community Ecology Further Reading Chapter Summary Monte Carlo Methods Monte Carlo Tests Generalized Monte Carlo Tests Implicit Statistical Models Applications of Monte Carlo Methods in Biology Single Species Ecology Chapter Summary Some General Considerations Questions about Computer-Intensive Methods Power Number of Random Sets of Data Needed for a Test Determining a Randomization Distribution Exactly The number of replications for confidence intervals More Efficient Bootstrap Sampling Methods The Generation of Pseudo-Random Numbers The Generation of Random Permutations Chapter Summary One and Two Sample Tests The Paired Comparisons Design The One Sample Randomization Test The Two Sample Randomization Test Bootstrap Tests Randomizing Residuals Comparing the Variation in Two Samples A Simulation Study The Comparison of Two Samples on Multiple Measurements Further Reading Chapter Summary Exercises Analysis of Variance One Factor Analysis of Variance Tests for Constant Variance Testing for Mean Differences Using Residuals Examples of More Complicated Types of Analysis of Variance Procedures for Handling Unequal Group Variances Other Aspects of Analysis of Variance Further Reading Chapter Summary Exercises Regression Analysis Simple Linear Regression Randomizing Residuals Testing for a Non-Zero B Value Confidence Limits for B Multiple Linear Regression Alternative Randomization Methods with Multiple Regression Bootstrapping and Jackknifing with Regression Further Reading Chapter Summary Exercises Distance Matrices and Spatial Data Testing for Association between Distance Matrices The Mantel Test Sampling the Randomization Distribution Confidence Limits for Regression Coefficients The Multiple Mantel Test Other Approaches with More than Two Matrices Further Reading Chapter Summary Exercises Other Analyses on Spatial Data Spatial Data Analysis The Study of Spatial Point Patterns Mead's Randomization Test Tests for Randomness Based on Distances Testing for an Association between Two Point Patterns The Besag-Diggle Test Tests Using Distances between Points Testing for Random Marking Further Reading Chapter Summary Exercises Time Series Randomization and Time Series Randomization Tests for Serial Correlation Randomization T ests for Trend Randomization Tests for Periodicity Irregularly Spaced Series Tests on Times of Occurrence Discussion on Procedures for Irregular Series Bootstrap and Monte Carlo Tests Further Reading Chapter Summary Exercises Multivariate Data Univariate and Multivariate Tests Sample Means and Covariance Matrices Comparison of Sample Mean Vectors Chi-Squared Analyses for Count Data Principle Component Analysis and Other One Sample Methods Discriminant Function Analysis Further Reading Chapter Summary Exercises Survival and Growth Data Bootstrapping Survival Data Bootstrapping for Variable Selection Bootstrapping for Model Selection Group Comparisons Growth Data Further Reading Chapter Summary Exercises Non-Standard Situations The Construction of Tests in Non-Standard Situations Species Co-Occurrences on Islands An Alternative Generalized Monte Carlo Test Examining Time Changes in Niche Overlap Probing Multivariate Data with Random Skewers Ant Species Sizes in Europe Chapter Summary Bayesian Methods The Bayesian Approach to Data Analysis The Gibbs Sampler and Related Methods Biological Applications Further Reading Chapter Summary Exercises Conclusion and Final Comments Randomization Bootstrapping Monte Carlo Methods in General Classical versus Bayesian Inference Appendix Software for Computer Intensive Statistics References Index
4,706 citations
Book•
21 Jul 2008
TL;DR: This paper presents a meta-modelling framework for estimating the values of node-based measures and describes its use in a number of real-world situations.
Abstract: Preface vii Chapter 1: Introduction to Social Networks 1 Chapter 2: Data Collection 19 Chapter 3: Visual Exploration 42 Chapter 4: Node-Based Measures 64 Chapter 5: Statistical Tests of Node-Based Measures 88 Chapter 6: Searching for Substructures 117 Chapter 7: Comparing Networks 141 Chapter 8: Conclusions 163 Glossary of Frequently Used Terms 173 References 175 Index 187
655 citations
TL;DR: SOCPROG is a set of programs which analyses data on animal associations, including mark-recapture population analyses and movement analyses, written in the programming language MATLAB and may be downloaded free from the World Wide Web.
Abstract: SOCPROG is a set of programs which analyses data on animal associations. Data usually come from observations of the social behaviour of individually identifiable animals. Associations among animals, sampling periods, restrictions on the data and association indices can be defined very flexibly. SOCPROG can analyse data sets including 1,000 or more individuals. Association matrices are displayed using sociograms, principal coordinates analysis, multidimensional scaling and cluster analyses. Permutation tests, Mantel and related tests and matrix correlation methods examine hypotheses about preferred associations among individuals and classes of individual. Weighted network statistics are calculated and can be tested against null hypotheses. Temporal analyses include displays of lagged association rates (rates of reassociation following an association). Models can be fitted to lagged association rates. Multiple association measures, including measures produced by other programs such as genetic or range use data, may be analysed using Mantel tests and principal components analysis. SOCPROG also performs mark-recapture population analyses and movement analyses. SOCPROG is written in the programming language MATLAB and may be downloaded free from the World Wide Web.
644 citations
"Animal social network inference and..." refers background or methods in this paper
...Yet with the exception of SOCPROG (Whitehead 2009), I am unaware of another package that will accept group data and generate a social network with a chosen measure (see Whitehead 2008, for information on index ratios)....
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...The need for specialized methods for analyses in this subject was rapidly addressed by statisticians and biologists, culminating in the package SOCPROG (Whitehead 2009) that provides routines for many complex analyses....
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TL;DR: An adaptation of a test developed by Manly, which uses the observed association data as a basis for a computer-generated randomization, demonstrated that populations with similar median HWI values showed clear differences in association patterns, that is, some were associating nonrandomly whereas others were not.
472 citations
"Animal social network inference and..." refers background or methods in this paper
...It was proposed by Bejder et al. (1998) that to avoid biases in sampling, randomizations should be performed on the data stream rather than on the association matrix....
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...These swaps can maintain the variance in individual gregariousness and size of each group constant (Bejder et al. 1998)....
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...This has spawned extensive literature, in particular when testing for statistical significance and non-randomness (Whitehead 1997; Bejder et al. 1998; Croft et al. 2008; Whitehead 2008; Croft et al. 2011)....
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...Performing permutation tests on the observation stream following the method originally proposed by Bejder et al. (1998) and since refined by other authors (Whitehead 2008; Sundaresan et al. 2009)....
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...The Bejder et al. (1998) permutationmethod is a useful way of estimating the significance of parameter estimates against biologically relevant null models, because permutations can control for spatial, temporal and individual variation....
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