1. Ecological research and management programs are increasingly using autonomous monitoring units (AMUs) to collect large volumes of acoustic and/or photo data to address pressing management objectives or research goals. The data management requirements of an AMU‐based monitoring effort are often overwhelming, with a considerable amount of processing to translate raw data into models and analyses that have research and management utility.
2. We created the r package AMMonitor to simplify the process of moving from remotely collected data to analysis and results, using a comprehensive SQLite database for data management that tracks all components of a remote monitoring program. This framework enables the tracking of analyses and research/management objectives through time.
3. We illustrate the AMMonitor approach with the example of evaluating an occurrence‐based management objective for a target species. First, we provide an overview of the database and data management approach. Next, we illustrate a few available workflows: temporally adaptive sampling, automated detection of species sounds from acoustic recordings and aggregation of automated detections into an encounter history for use in an occupancy analysis, the outcome of which can be analysed with respect to the motivating management objective.
4. Without a comprehensive framework for efficiently moving from raw remote monitoring data collection to results and analysis, monitoring programs are limited in their capacity to systematically characterize ecological processes and inform management decisions through time. AMMonitor provides an option for such a framework. Code, comprehensive documentation and step‐by‐step examples are available online at https://code.usgs.gov/vtcfwru/AMMonitor

中文翻译：

---

AMMonitor：在具有R的自适应框架中远程监测生物多样性

1. 生态研究和管理计划越来越多地使用自主监测单元（AMU）来收集大量的声音和/或照片数据，以解决紧迫的管理目标或研究目标。基于AMU的监视工作对数据管理的要求通常是压倒性的，需要进行大量处理以将原始数据转换为具有研究和管理实用性的模型和分析。
2. 我们创建了r包AMM onitor，以使用全面的SQLite数据库进行数据管理并跟踪远程监视程序的所有组件，从而简化了从远程收集的数据到分析和结果的转换过程。该框架可以随时跟踪分析和研究/管理目标。
3. 我们以评估目标物种基于事件的管理目标为例，说明了AMM监督者方法。首先，我们概述了数据库和数据管理方法。接下来，我们说明一些可用的工作流程：时间自适应采样，从声学记录中自动检测物种声音，以及将自动检测汇总为遇到历史以用于占用分析，可以针对激励管理来分析其结果目的。
4. 如果没有一个全面的框架来有效地从原始的远程监测数据收集转移到结果和分析，则监测程序的能力有限，无法系统地表征生态过程并随时间告知管理决策。AMM onitor为此类框架提供了一个选项。可从https://code.usgs.gov/vtcfwru/AMMonitor在线获取代码，全面的文档和分步示例。