ATfiltR: A solution for managing and filtering detections from passive acoustic telemetry data

Area of Science:

• Ecology
• Animal Behavior
• Data Science

Background:

• Acoustic telemetry is widely used for aquatic animal movement tracking.
• Data from acoustic telemetry frequently contains spurious detections, complicating analysis.
• Manual data management for large telemetry datasets exceeds spreadsheet capabilities.

Purpose of the Study:

• To introduce ATfiltR, an open-source R package for managing and filtering acoustic telemetry data.
• To provide researchers with a tool for enhancing the accuracy and reproducibility of telemetry studies.
• To automate the identification and exclusion of spurious detections.

Main Methods:

• ATfiltR integrates diverse telemetry files into a unified dataset.
• The package allows conditional attribution of animal and location data to detections.
• Customizable rules are applied to filter out spurious detections.

Main Results:

• ATfiltR compiles telemetry data, isolating detections outside study periods.
• It identifies and separates unique, potentially erroneous, detections within specified timeframes.
• The package flags detections inconsistent with animal swimming speeds and receiver range.

Conclusions:

• ATfiltR offers a robust solution for managing and cleaning large acoustic telemetry datasets.
• This tool enhances the reliability of animal movement data derived from acoustic telemetry.
• ATfiltR is expected to benefit researchers, particularly those new to acoustic telemetry, by improving results reproducibility.