Clarfeld, L., C. Tang, C. Balantic, K. Huber, and T. Donovan. 2025. AMMonitor 2.0: Remote monitoring of biodiversity in an adaptive framework in R. Methods in Ecology and Evolution. DOI: 10.1111/2041-210X.14487
Abstract
1. Wildlife face a myriad of challenges due to changing climate and land use regimes that necessitate efficient monitoring methods on large geospatial scales. This monitoring is increasingly accomplished with remotely deployed trail cameras and automated recording units.
2. However, remote wildlife monitoring comes with data management challenges, such as storing, organizing and analysing large volumes of digital data. Data management tools can assist biologists in managing projects to reduce the barriers between data collection and dissemination of results.
3. AMMonitor is an R package for remote wildlife monitoring with greatly increased functionality in the version 2 release, including a new database structure, support for both photographs and audio monitoring, a rich graphical user interface (Shiny) that includes media labelling tools, integration of machine learning classification outputs and 20 in-depth tutorials.
4. As a fully open-source data management solution, AMMonitor is highly accessible, extensible and customizable, making it especially useful for students and users with some coding experience who desire a standardized yet flexible data management framework.
5. AMMonitor project data can be released to the public via the USGS ScienceBase data repository, where released projects can be reconstituted by AMMonitor functions.
2. However, remote wildlife monitoring comes with data management challenges, such as storing, organizing and analysing large volumes of digital data. Data management tools can assist biologists in managing projects to reduce the barriers between data collection and dissemination of results.
3. AMMonitor is an R package for remote wildlife monitoring with greatly increased functionality in the version 2 release, including a new database structure, support for both photographs and audio monitoring, a rich graphical user interface (Shiny) that includes media labelling tools, integration of machine learning classification outputs and 20 in-depth tutorials.
4. As a fully open-source data management solution, AMMonitor is highly accessible, extensible and customizable, making it especially useful for students and users with some coding experience who desire a standardized yet flexible data management framework.
5. AMMonitor project data can be released to the public via the USGS ScienceBase data repository, where released projects can be reconstituted by AMMonitor functions.