Abstract
Recent advances in tracking technologies such as GPS or video tracking systems describe the movement paths of individuals in unprecedented details and are increasingly used in different fields, including ecology. However, extracting information from raw movement data requires advanced analysis techniques, for instance to infer behaviors expressed during a certain period of the recorded trajectory, or gender or species identity in case data is obtained from remote tracking. In this paper, we address how different movement features affect the ability to automatically classify the species identity, using a dataset of unicellular microbes (i.e., ciliates). Previously, morphological attributes and simple movement metrics, such as speed, were used for classifying ciliate species. Here, we demonstrate that adding advanced movement features, in particular such based on discrete wavelet transform, to morphological features can improve classification. These results may have practical applications in automated monitoring of waste water facilities as well as environmental monitoring of aquatic systems.
Highlights
Over the past decades, various tracking technologies such as the Global Positioning System (GPS) and sophisticated video techniques have become accessible to scientists and enabled the recording of large amounts of data about the movement paths of individual organisms [1,2,3,4]
The baseline Morphology model is quite successful in classifying most of the species, except for Blepharisma, C. campylum and P. aurelia which have low recall and precision values in both support vector machines (SVM) and decision trees (DT) cases (Fig 2a)
The Morphology model based on SVM reaches a classification accuracy of 86% and Kappa value of 0.82, which is comparable to the result of the decision tree with an accuracy of 85% and Kappa of 0.81 (Fig 2a)
Summary
Various tracking technologies such as the Global Positioning System (GPS) and sophisticated video techniques have become accessible to scientists and enabled the recording of large amounts of data about the movement paths of individual organisms [1,2,3,4]. GPS tags or collars have the advantage that auxiliary information on the individual can be collected when the device is attached, which can subsequently help in understanding the differences between collected movement paths. For inferring other sorts of information such as gender or species, remote techniques such as video tracking are neither capturing nor marking the individual and auxiliary information on the species or gender of the tracked individual is not known. Previous studies found that it is possible to PLOS ONE | DOI:10.1371/journal.pone.0145345 December 17, 2015
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