A new framework for analysing automated acoustic species detection data: occupancy estimation and optimization of recordings post-processing

The development and use of automated species detection technologies, such as acoustic recorders, for monitoring wildlife are rapidly expanding. Automated classification algorithms provide cost- and time-effective means to process information-rich data, but often at the cost of additional detection errors. Appropriate methods are necessary to analyse such data while dealing with the different types of detection errors. We developed a hierarchical modelling framework for estimating species occupancy from automated species detection data. We explore design and optimization of data post-processing procedures to account for detection errors and generate accurate estimates. Our proposed method accounts for both imperfect detection and false-positive errors and utilizes information about both occurrence and abundance of detections to improve estimation. Using simulations, we show that our method provides much more accurate estimates than models ignoring the abundance of detections. The same fi...用于野生动物监测的自动化物种检测技术（如声学记录仪(acoustic recorders)）的开发与应用正呈快速扩张之势。自动化分类算法可为处理高信息量数据提供兼具成本效益与时间效益的解决方案，但往往会引入额外的检测误差。对此类数据进行分析时，亟需适配的方法以应对各类检测误差。我们开发了一套层级化建模框架，用于从自动化物种检测数据中估算物种占用率(species occupancy)。本研究探究了数据后处理流程的设计与优化方案，以抵消检测误差的影响并生成精准的估算结果。我们提出的方法同时兼顾不完全检测与假阳性误差，并利用检测事件的发生情况与丰度信息提升估算精度。通过模拟实验证实，相较于忽略检测丰度的模型，本方法能够提供更为精准的估算结果。