1. Growing interest in the structure and dynamics of animal social networks has stimulated efforts to develop automated tracking technologies that can reliably record encounters in free‐ranging subjects. A particularly promising approach is the use of animal‐attached ‘proximity loggers’, which collect data on the incidence, duration and proximity of spatial associations through inter‐logger radio communication. While proximity logging is based on a straightforward physical principle – the attenuation of propagating radio waves with distance – calibrating systems for field deployment is challenging, since most study species roam across complex, heterogeneous environments.
2. In this study, we calibrated a recently developed digital proximity‐logging system (‘Encounternet’) for deployment on a wild population of New Caledonian crows Corvus moneduloides. Our principal objective was to establish a quantitative model that enables robust post hoc estimation of logger‐to‐logger (and, hence, crow‐to‐crow) distances from logger‐recorded signal‐strength values. To achieve an accurate description of the radio communication between crow‐borne loggers, we conducted a calibration exercise that combines theoretical analyses, field experiments, statistical modelling, behavioural observations, and computer simulations.
3. We show that, using signal‐strength information only, it is possible to assign crow encounters reliably to predefined distance classes, enabling powerful analyses of social dynamics. For example, raw data sets from field‐deployed loggers can be filtered at the analysis stage to include predominantly encounters where crows would have come to within a few metres of each other, and could therefore have socially learned new behaviours through direct observation. One of the main challenges for improving data classification further is the fact that crows – like most other study species – associate across a wide variety of habitats and behavioural contexts, with different signal‐attenuation properties.
4. Our study demonstrates that well‐calibrated proximity‐logging systems can be used to chart social associations of free‐ranging animals over a range of biologically meaningful distances. At the same time, however, it highlights that considerable efforts are required to conduct study‐specific system calibrations that adequately account for the biological and technological complexities of field deployments. Although we report results from a particular case study, the basic rationale of our multi‐step calibration exercise applies to many other tracking systems and study species.

中文翻译：

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校准动物传播的接近记录仪。

1. 人们对动物社交网络的结构和动态的兴趣日益浓厚，刺激了人们开发自动跟踪技术的努力，这些技术可以可靠地记录自由活动对象的遭遇。一种特别有前途的方法是使用动物附着的“邻近记录器”，它通过记录器间的无线电通信收集有关空间关联的发生率、持续时间和邻近度的数据。虽然近距离测井基于简单的物理原理（传播的无线电波随距离衰减），但用于现场部署的校准系统具有挑战性，因为大多数研究物种都在复杂、异构的环境中漫游。
2. 在这项研究中，我们校准了最近开发的数字邻近记录系统（“Encounternet”），用于部署在新喀里多尼亚乌鸦Corvus moneduloides的野生种群上。我们的主要目标是建立一个定量模型，能够根据记录仪记录的信号强度值对记录仪到记录仪（以及乌鸦到乌鸦）的距离进行稳健的事后估计。为了准确描述鸦传记录仪之间的无线电通信，我们进行了一次校准练习，结合了理论分析、现场实验、统计建模、行为观察和计算机模拟。
3. 我们证明，仅使用信号强度信息，就可以将乌鸦的遭遇可靠地分配给预定义的距离类别，从而实现对社会动态的强大分析。例如，来自现场部署的记录仪的原始数据集可以在分析阶段进行过滤，以主要包括乌鸦彼此相距几米之内的遭遇，因此可以通过直接观察在社会上学习新的行为。进一步改进数据分类的主要挑战之一是，乌鸦与大多数其他研究物种一样，与各种栖息地和行为环境相关联，具有不同的信号衰减特性。
4. 我们的研究表明，经过良好校准的邻近记录系统可用于绘制一系列具有生物学意义的距离内自由放养动物的社会关联。然而，与此同时，它强调需要付出相当大的努力来进行特定于研究的系统校准，以充分考虑现场部署的生物和技术复杂性。尽管我们报告了特定案例研究的结果，但我们的多步骤校准练习的基本原理适用于许多其他跟踪系统和研究物种。