Hypothesis testing in animal social networks.

By Darren P. Croft, J. Madden, Daniel W. Franks, Richard James. Trends in Ecology & Evolution, 2011

399 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics

View Paper: https://www.semanticscholar.org/paper/049f7f347590df624db8ca5f13242af18d801d3c

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Citations in the corpus

Constructing, conducting and interpreting animal social network analysis (2015, methodology): “Network measures are inherently non-independent and thus violate the assumptions underlying most parametric statistical tests (Croft et al. 2011b).”
Social network analysis of mixed-species flocks: exploring the structure and evolution of interspecific social behaviour (2012, methodology): “At each level, comparisons can be made with null models of randomized networks (Bejder et al. 1998; Whitehead 2008; Croft et al. 2011) to test for nonrandom characteristics of the observed social structure.”
Social behaviour in sharks and rays: analysis, patterns and implications for conservation (2012, background): “Care needs to be taken however, when testing hypotheses using social network data as relational data are non-independent and thus often violate the assumptions of common statistical approaches (see Croft et al. 2011 for a discussion and a review of approaches to overcome this issue).”
Shark personalities? Repeatability of social network traits in a widely distributed predatory fish (2014, methodology): “Non-orthogonal network data is problematic to analyse statistically (Croft et al. 2011), and in an attempt to overcome this, a randomisation procedure was devised (Wilson et al. 2013).”
Network position: a key component in the characterization of social personality types (2012, background): “While it is possible to account for some aspects of non-independence of individuals in networks using such models, it remains an open question whether this is generally the case (Croft et al. 2011).”
Developing a deeper understanding of animal movements and spatial dynamics through novel application of network analyses (2012, background): “As such, current statistical approaches largely rely on generating null network populations by randomly permuting various aspects of the data set (Croft et al. 2011).”
Trends and perspectives on the use of animal social network analysis in behavioural ecology: a bibliometric approach (2019, methodology): “Although animal social network analysis is an important method for testing hypotheses about animal social structure (Croft et al., 2011), it is also relevant in an applied context (Makagon, McCowan, & Mench, 2012).”
Animal social network inference and permutations for ecologists in R using asnipe (2013, background): “However, numerous studies are still published using packages such as UCINET (Borgatti et al. 2002) that provide out-of-the-box analyses but typically violate many of the underlying assumptions from data sampling when calculating signiﬁcance in animal social networks (Croft et al. 2011).”
The dynamics of animal social networks: analytical, conceptual, and theoretical advances (2014, neutral): “Multiple Regression Quadratic Assignment Procedure (MRQAP) is another method that can be used to investigate the synergistic effects of multiple factors on network structure (Dekker et al. 2003; Dekker et al. 2007; Croft et al. 2011).”
Social network correlates of food availability in an endangered population of killer whales, Orcinus orca (2012, positive): “To account for autocorrelations in our network data, and to control for group size distributions, demographic effects and sampling, we used a randomization test to calculate significance (Bräger et al. 1994; Whitehead 1999; Croft et al. 2011).”
A guide to null models for animal social network analysis (2017, methodology): “I do not go into further detail, as their use is relatively uncommon and not applicable to the data I present here, but further discussion can be found in Croft et al. (2011).”