Our review looks at recent advances in technologies applied to studying pollinators in the field. These include RFID, radar and lidar for detecting and tracking pollinators; wireless sensor networks (e.g. ‘smart’ hives); automated visual and audio monitoring systems including vision motion software for monitoring fine-scale pollinator behaviours over extended periods; and automated species identification systems based on machine learning that can vastly reduce the bottleneck in (big) data analysis. An improved e-ecology platform that leverages these tools is needed for ecologists to acquire and understand large spatiotemporal datasets, and thus inform knowledge gaps in environmental policy-making. Developing the next generation of e-ecology tools will require synergistic partnerships between academia and industry and significant investment in a cross-disciplinary scientific consortia.

我们的评论着眼于研究该领域传粉媒介的技术的最新进展。其中包括用于检测和跟踪传粉媒介的RFID，雷达和激光雷达；无线传感器网络（例如“智能”蜂箱）；自动化的视听监控系统，包括视觉运动软件，可长时间监控精细授粉媒介的行为；以及基于机器学习的自动物种识别系统，可以大大减少（大）数据分析中的瓶颈。生态学家需要一个利用这些工具的改进的生态平台，以获取和理解较大的时空数据集，从而弥补环境政策制定过程中的知识空白。