Vander Wal, E. | bioRxiv Science

bioRxiv2018

Conducting social network analysis with animal telemetry data: applications and methods using spatsoc

O_LIWe present spatsoc: an R package for conducting social network analysis with animal telemetry data.\nC_LIO_LIAnimal social network analysis is a method for measuring relationships between individuals to describe social structure. Using animal telemetry data for social network analysis requires functions to generate proximity-based social networks that have flexible temporal and spatial grouping. Data can be complex and relocation frequency can vary so the ability to provide specific temporal and spatial thresholds based on the characteristics of the species and system is required.\nC_LIO_LIspatsoc fills a gap in R packages by providing flexible functions, explicitly for animal telemetry data, to generate gambit-of-the-group data, perform data-stream randomization and generate group by individual matrices.\nC_LIO_LIThe implications of spatsoc are that current users of large animal telemetry or otherwise georeferenced data for movement or spatial analyses will have access to efficient and intuitive functions to generate social networks.\nC_LI

ecology↗

bioRxiv2018

Trends and perspectives on the use of social network analysis in behavioural ecology: a bibliometric approach

The increased popularity and improved accessibility of social network analysis has improved our ability to test hypotheses about the complexity of animal social structure. To gain a deeper understanding of the use and application of social network analysis, we systematically surveyed the literature and extracted information on publication trends from articles using social network analysis. We synthesize trends in social network research over time and highlight variation in the use of different aspects of social network analysis. Our primary finding highlights the increase in use of social network analysis over time and from this finding, we observed an increase in the number of review and methods of social network analysis. We also found that most studies included a relatively small number (median = 15, range = 4-1406) of individuals to generate social networks, while the number and type of social network metrics calculated in a given study varied zero to nine (median = 2, range 0-9). The type of data collection or the software programs used to analyze social network data have changed; SOCPROG and UCINET have been replaced by various R packages over time. Finally, we found strong taxonomic and conservation bias in the species studied using social network analysis. Most species studied using social networks are mammals (111/201, 55%) or birds (47/201, 23%) and the majority tend to be species of least concern (119/201, 59%). We highlight emerging trends in social network research that may be valuable for distinct groups of social network researchers: students new to social network analysis, experienced behavioural ecologists interested in using social network analysis, and advanced social network users interested in trends of social network research. In summary we address the temporal trends in social network publication practices, highlight potential bias in some of the ways we employ social network analysis, and provide recommendations for future research based on our findings.

animal behavior and cognition↗