BIOTAS: BIOTelemetry Analysis Software, for the semi-automated removal of false positives from radio telemetry data

Introduction

Radio telemetry, one of the most widely used techniques for tracking wildlife and fisheries populations, has a false-positive problem. Bias from false-positive detections can affect many important derived metrics, such as home range estimation, site occupation, survival, and migration timing. False-positive removal processes have relied upon simple filters and personal opinion. To overcome these shortcomings, we have developed BIOTAS (BIOTelemetry Analysis Software) to assist with false-positive identification, removal, and data management for large-scale radio telemetry projects.

Methods

BIOTAS uses a naïve Bayes classifier to identify and remove false-positive detections from radio telemetry data. The semi-supervised classifier uses spurious detections from unknown tags and study tags as training data. We tested BIOTAS on four scenarios: wide-band receiver with a single Yagi antenna, wide-band receiver that switched between two Yagi antennas, wide-band receiver with a single dipole antenna, and single-band receiver that switched between five frequencies. BIOTAS has a built in a k-fold cross-validation and assesses model quality with sensitivity, specificity, positive and negative predictive value, false-positive rate, and precision-recall area under the curve. BIOTAS also assesses concordance with a traditional consecutive detection filter using Cohen’s κκ.

Results

Overall BIOTAS performed equally well in all scenarios and was able to discriminate between known false-positive detections and valid study tag detections with low false-positive rates (