Proximity data-loggers increase the quantity and quality of social network data
Proximity data-loggers increase the quantity and quality of social network data
复制标题
接近数据记录器提高了社交网络数据的数量和质量
作者：
T. Ryder;B. Horton;Mike van den Tillaart;J. D. Morales;I. Moore
社会网络分析是一种理想的定量工具，可以促进我们对复杂社会行为的理解。然而，这种方法往往受到准确描述社会结构和测量网络异质性的挑战的限制。技术进步促进了对社交网络的研究，但迄今为止，所有这些工作都集中在大型脊椎动物上。在这里，我们提供的概念证明使用接近数据记录，以量化的频率的社会互动，构建加权网络和特征的变化，在社会行为的一个lek繁殖的鸟，线尾侏儒鸟，Pipra filicauda。我们的研究结果强调了这种方法如何通过同时提高数据质量和数量来改善社交网络数据收集和分析的挑战。
Social network analysis is an ideal quantitative tool for advancing our understanding of complex social behaviour. However, this approach is often limited by the challenges of accurately characterizing social structure and measuring network heterogeneity. Technological advances have facilitated the study of social networks, but to date, all such work has focused on large vertebrates. Here, we provide proof of concept for using proximity data-logging to quantify the frequency of social interactions, construct weighted networks and characterize variation in the social behaviour of a lek-breeding bird, the wire-tailed manakin, Pipra filicauda. Our results highlight how this approach can ameliorate the challenges of social network data collection and analysis by concurrently improving data quality and quantity.