Recursive filtering for zero offset correction of diving depth time series with GNU R package diveMove

• Published in: PloS one Vol. 6; no. 1; p. e15850
• Main Authors: Luque, Sebastián P, Fried, Roland
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 28-01-2011; Public Library of Science (PLoS)
• Subjects: Analysis; Animal behavior; Animals; Arctocephalus gazella; Biology; Computer programs; Computer Science; Datasets; Depth recorders; Diving; Electronic filters; Engineering; Equipment Design; Error correction; Filtration; Foraging behavior; Fur Seals - physiology; Marine; Noise; Performance evaluation; Quantiles; Recorders; Reptiles & amphibians; Research Design; Signal Processing, Computer-Assisted; Software; Studies; Time Factors; Time series; Transducers
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0015850

Zero offset correction of diving depth measured by time-depth recorders is required to remove artifacts arising from temporal changes in accuracy of pressure transducers. Currently used methods for this procedure are in the proprietary software domain, where researchers cannot study it in sufficient detail, so they have little or no control over how their data were changed. GNU R package diveMove implements a procedure in the Free Software domain that consists of recursively smoothing and filtering the input time series using moving quantiles. This paper describes, demonstrates, and evaluates the proposed method by using a "perfect" data set, which is subsequently corrupted to provide input for the proposed procedure. The method is evaluated by comparing the corrected time series to the original, uncorrupted, data set from an Antarctic fur seal (Arctocephalus gazella Peters, 1875). The Root Mean Square Error of the corrected data set, relative to the "perfect" data set, was nearly identical to the magnitude of noise introduced into the latter. The method, thus, provides a flexible, reliable, and efficient mechanism to perform zero offset correction for analyses of diving behaviour. We illustrate applications of the method to data sets from four species with large differences in diving behaviour, measured using different sampling protocols and instrument characteristics.