MAST (Movement Analysis Software for Telemetry data), for the semi-automated removal of false positives from radio telemetry data

Abstract

IntroductionRadio 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 MAST (Movement Analysis Software for Telemetry data) to assist with false-positive identification, removal, and data management for large-scale radio telemetry projects.MethodsMAST 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 MAST 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. MAST 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. MAST also assesses concordance with a traditional consecutive detection filter using Cohen’s κ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\kappa$$\\end{document}.ResultsOverall MAST 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 (< 0.001) as determined through cross-validation, even as receivers switched between antennas and frequencies. MAST classified between 94 and 99% of study tag detections as valid.ConclusionAs part of a robust data management plan, MAST is able to discriminate between detections from study tags and known false positives. MAST works with multiple manufacturers and accounts for receivers that switch between antennas and frequencies. MAST provides the framework for transparent, objective, and repeatable telemetry projects for wildlife conservation surveys, and increases the efficiency of data processing.