Validation of an automated data collection method for quantifying social networks in collective behaviours
Validation of an automated data collection method for quantifying social networks in collective behaviours
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DOI：
10.1007/s00265-014-1757-0
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发表时间：
2014-07
影响因子：
2.3
通讯作者：
Fumiaki Y. Nomano;L. E. Browning;Shinichi Nakagawa;S. Griffith;A. Russell
中科院分区：
文献类型：
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作者：
Fumiaki Y. Nomano;L. E. Browning;Shinichi Nakagawa;S. Griffith;A. Russell
群体成员之间偏好的社会网络可以影响集体行为的分布和后果。然而，描述社会网络结构的行为背景和分类仍然有限，因为这类研究需要广泛的数据。在这里，我们强调使用自动无源集成应答器(PIT)标签监测系统进行社会网络分析，并在一种新的背景下这样做-在鸟类合作育种者中进行筑巢供应，对于这种情况，直接观察社会行为是困难的。首先，我们使用观察者和摄像机在坑标数据中得出适当的巢访问同步性度量。其次，我们使用内部Nest摄像头验证了该指标在社交网络分析中的使用。第三，我们使用带有社会性参数的层次回归模型来研究从多个群体收集的网络结构。使用坑标可以高精度地获得所有种群成员的探巢持续时间和频率，但繁殖的雌性除外，由于其探视时间的高度变异性，需要使用摄像机进行准确的估计。坑标签数据集揭示了社交网络结构的显著变异性。我们的结果强调了在对野生动物进行社会网络分析时结合互补观察方法的重要性。我们的方法也可以推广到社会系统中的多种情况，只要在封闭空间中与其他个人的反复相遇会产生生态影响。
The social network of preferences among group members can affect the distribution and consequences of collective behaviours. However, the behavioural contexts and taxa in which social network structure has been described are still limited because such studies require extensive data. Here, we highlight the use of an automated passive integrated transponder (PIT)-tag monitoring system for social network analyses and do so in a novel context—nestling provisioning in an avian cooperative breeder, for which direct observation of social behaviours is difficult. First, we used observers and cameras to arrive at a suitable metric of nest visit synchrony in the PIT-tag data. Second, we validated the use of this metric for social network analyses using internal nest video cameras. Third, we used hierarchical regression models with ‘sociality’ parameter to investigate structure of networks collected from multiple groups. Use of PIT tags led to nest visitation duration and frequency being obtained with a high degree of accuracy for all group members, except for the breeding female for whom accurate estimations required the use of a video camera due to her high variability in visitation time. The PIT-tag dataset uncovered significant variability in social network structure. Our results highlight the importance of combining complementary observation methods when conducting social network analyses of wild animals. Our methods can also be generalised to multiple contexts in social systems wherever repeated encounters with other individuals in closed space have ecological implications.