30 research outputs found
A movement ecology toolkit : Novel biotelemetry methodologies for elucidating animal behaviour and location.
Path segmentation for beginners: an overview of current methods for detecting changes in animal movement patterns
Correction: On Higher Ground: How Well Can Dynamic Body Acceleration Determine Speed in Variable Terrain?
A Web-based semantic tagging and activity recognition system for species' accelerometry data
Increasingly, animal biologists are taking advantage of low cost micro-sensor technology, by deploying accelerometers to monitor the behavior and movement of a broad range of species. The result is an avalanche of complex tri-axial accelerometer data streams that capture observations and measurements of a wide range of animal body motion and posture parameters. Analysis of these parameters enables the identification of specific animal behaviors - however the analysis process is immature with much of the activity identification steps undertaken manually and subjectively. Consequently, there is an urgent need for the development of new tools to streamline the management, analysis, indexing, querying and visualization of such data. In this paper, we present a Semantic Annotation and Activity Recognition (SAAR) system which supports storing, visualizing, annotating and automatic recognition of tri-axial accelerometer data streams by integrating semantic annotation and visualization services with Support Vector Machine (SVM) techniques. The interactive Web interface enables biologists to visualize and correlate 3D accelerometer data streams with associated video streams. It also enables domain experts to accurately annotate or tag segments of tri-axial accelerometer data streams, with standardized terms from an activity ontology. These annotated data streams can then be used to dynamically train a hierarchical SVM activity classification model, which can be applied to new accelerometer data streams to automatically recognize specific activities. This paper describes the design, implementation and functional details of the SAAR system and the results of the evaluation experiments that assess the performance, usability and efficiency of the system. The evaluation results indicate that the SAAR system enables ecologists with little knowledge of machine learning techniques to collaboratively build classification models with high levels of accuracy, sensitivity, precision and specificity
Corrigendum to “A web-based semantic tagging and activity recognition system for species' accelerometry data” [Ecol. Inf. 13 (2013) 47–56]
Correction: On Higher Ground: How Well Can Dynamic Body Acceleration Determine Speed in Variable Terrain?
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Spatial ecology of the Vicuña (Lama vicugna) in a high Andean protected area.
The study of animal space use is fundamental to effective conservation and management of wildlife populations and habitats in a rapidly changing world, yet many species remain poorly described. Such is the case for the spatial ecology of the Vicuña-a medium-sized wild camelid that plays a critical role, both as a consumer and as prey, in the high Andean food web. We studied patterns of space use of 24 adult female vicuñas from April 2014 to February 2017 at the southern edge of its range. Vicuñas showed strong fidelity to their home range locations across the study period and shared large portions of their home ranges with vicuñas from other family groups. Vicuña home ranges in our study were considerably larger than previous estimates across the range of the species. Variation in environmental and terrain factors and the associated risk of predation affected vicuña diel migration distance but not home range size or overlap. Our study offers new ecological insights into vicuña space use that can inform conservation and management efforts of vicuñas and other social ungulates
Linear regression between each metric and speed for each substrate/incline.
<p>(i) VeDBA, (ii) Amplitude, (iii) Peak Frequency and (iv) ODBA.</p
Summary of GLM statistics for each metric comparing the relationship with speed under each substrate/incline condition.
<p>Summary of GLM statistics for each metric comparing the relationship with speed under each substrate/incline condition.</p
Summary of the statistics for the regression between the metrics (VeDBA, ODBA, Peak Frequency and Amplitude) and speed, with data for all substrates/inclines collated.
<p>Summary of the statistics for the regression between the metrics (VeDBA, ODBA, Peak Frequency and Amplitude) and speed, with data for all substrates/inclines collated.</p