Application testing of a new three-dimensional acceleration measuring system with wireless data transfer (WAS) for behavior analysis

Scheibe, Klaus; Gromann, Cora

doi:10.3758/bf03192796

Cited by 44 publications

(33 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Best practices in data acquisition and data analysis: sampling axes, sampling interval and sampling frequency Sampling of all three axes of acceleration (tri-axial) is the most accurate and precise way of measuring behavior that occurs in three dimensions as well as estimating energy expenditure [20,161]. For some research questions or for relatively immobile species, one or two axes may be sufficient to characterize the behavior(s) of interest [23,80].…”

Section: Potential Application Of Accelerometry: Position and Locationmentioning

confidence: 99%

“…Because burst studies record fewer data over the entire study period it is possible to download the data remotely through wireless connections [21,40], whereas continuous accelerometer data typically are logged over days or weeks and manually downloaded upon tag retrieval [143]. If animals are expected to remain within the vicinity of a fixed receiver, then continuous data may be transmitted wirelessly at intervals [161]. If proximity to a receiver is problematic, as with marine animals that can range over very long distances, data can still be collected at high resolution (high sampling frequency and continuous sampling interval) as long as the entire sampling period matches device storage capacity, or there is periodic offloading of data via mobile receivers such as satellites.…”

Section: Potential Application Of Accelerometry: Position and Locationmentioning

confidence: 99%

See 1 more Smart Citation

Observing the unwatchable through acceleration logging of animal behavior

et al. 2013

View full text Add to dashboard Cite

Behavior is an important mechanism of evolution and it is paid for through energy expenditure. Nevertheless, field biologists can rarely observe animals for more than a fraction of their daily activities and attempts to quantify behavior for modeling ecological processes often exclude cryptic yet important behavioral events. Over the past few years, an explosion of research on remote monitoring of animal behavior using acceleration sensors has smashed the decades-old limits of observational studies. Animal-attached accelerometers measure the change in velocity of the body over time and can quantify fine-scale movements and body postures unlimited by visibility, observer bias, or the scale of space use. Pioneered more than a decade ago, application of accelerometers as a remote monitoring tool has recently surged thanks to the development of more accessible hardware and software. It has been applied to more than 120 species of animals to date. Accelerometer measurements are typically collected in three dimensions of movement at very high resolution (>10 Hz), and have so far been applied towards two main objectives. First, the patterns of accelerometer waveforms can be used to deduce specific behaviors through animal movement and body posture. Second, the variation in accelerometer waveform measurements has been shown to correlate with energy expenditure, opening up a suite of scientific questions in species notoriously difficult to observe in the wild. To date, studies of wild aquatic species outnumber wild terrestrial species and analyses of social behaviors are particularly few in number. Researchers of domestic and captive species also tend to report methodology more thoroughly than those studying species in the wild. There are substantial challenges to getting the most out of accelerometers, including validation, calibration, and the management and analysis of large quantities of data. In this review, we illustrate how accelerometers work, provide an overview of the ecological questions that have employed accelerometry, and highlight the emerging best practices for data acquisition and analysis. This tool offers a level of detail in behavioral studies of free-ranging wild animals that has previously been impossible to achieve and, across scientific disciplines, it improves understanding of the role of behavioral mechanisms in ecological and evolutionary processes. AbstractResumen: El comportamiento es un mecanismo importante de la evolución y que se paga a través del gasto de energía. Sin embargo, los biólogos de campo raramente observan los animales durante más de una fracción de sus actividades y los intentos de cuantificar el comportamiento para el modelado de los procesos ecológicos a menudo excluyen eventos crípticos pero importantes. En los últimos años se produjeron avances importantes en el monitoreo remoto del comportamiento de los animales, utilizando sensores de telemétro de aceleración (acelerómetros) que empujan los límites tradicionales de los estudios observacionales. Acelerómetros uni...

show abstract

Section: Potential Application Of Accelerometry: Position and Locationmentioning

confidence: 99%

Section: Potential Application Of Accelerometry: Position and Locationmentioning

confidence: 99%

Observing the unwatchable through acceleration logging of animal behavior

et al. 2013

View full text Add to dashboard Cite

show abstract

“…The Martiskainen et al energy measure was designed to look at periodic behavior of their data and, in a prior paper, it was determined to be the least important of the summary statistics that were examined (Ravi et al, 2005). Other researchers have looked at relating frequency analyses of ACC data to observed behaviors (Sakamoto et al, 2009;Scheibe and Gromann, 2006;Watanabe et al, 2005). We visually inspected the power spectrum of the ACC time series but could not identify strong patterns in the frequency domain.…”

Section: Summary Statistics Selectionmentioning

confidence: 99%

“…Possibly, the granularity of our measurements was too coarse (3.33Hz per axis) to identify these. Studies relating frequency to behavior have used per-axis frequencies ranging from 64Hz (Sakamoto et al, 2009) through 33.3Hz (Scheibe and Gromann, 2006) to 16Hz (Watanabe et al, 2005), which are significantly higher than our sampling frequency and allow much finer determination of frequency behavior. In general, sampling frequency should be at least twice the frequency of the most rapid body movement essential to characterize a behavioral mode to fulfil the Nyquist sampling criterion (Chen and Bassett, 2005).…”

Section: Summary Statistics Selectionmentioning

confidence: 99%

“…The use of accelerometers for studying movement of organisms stems from epidemiological studies, originated in the 1950s, aimed at assessing changes in human physical activity in relation to health status (Chen and Bassett, 2005;Plasqui and Westerterp, 2007;Yang and Hsu, 2010). The technique has been applied more recently to study the movement, behavior and physiology of animals as well (Fahlman et al, 2008;Gleiss et al, 2010;Green et al, 2009b;Halsey et al, 2009a;Halsey et al, 2008;Halsey et al, 2009b;Halsey et al, 2011;Martiskainen et al, 2009;Moreau et al, 2009;Payne et al, 2011;Sakamoto et al, 2009;Scheibe and Gromann, 2006;Shepard et al, 2009a;Shepard et al, 2009b;Shepard et al, 2008;Watanabe et al, 2005;Wilson et al, 2006;Yoda et al, 2001;Yoda et al, 1999).Accelerometers provide measures of two distinct types of acceleration: static and dynamic acceleration. Static acceleration is due to the force of the gravitation field of the Earth (and the orientation of the accelerometer with respect to that field), whereas dynamic acceleration is due to animal movement .…”

mentioning

confidence: 99%

See 1 more Smart Citation

Using tri-axial acceleration data to identify behavioral modes of free-ranging animals: general concepts and tools illustrated for griffon vultures

Nathan

Spiegel

Fortmann-Roe

et al. 2012

Journal of Experimental Biology

399

530

View full text Add to dashboard Cite

Movement ecology and the integration of ecology, behavior and biomechanicsRecent advances in mechanistic modeling and tracking technology have enriched our capacity to disentangle the key parameters affecting movement processes and to characterize movement patterns accurately. These advances set the stage for integrating the four existing paradigms for studying movement -the random, biomechanical, cognitive and optimality approaches ( Fig.1) -in the form of a new cohesive 'movement ecology' framework . The biomechanical paradigm elucidates the machineries that enable individuals or propagules to move, including their physical mechanics, energetics and physiology, and thus focuses on the study of the motion capacity of individual organisms. The cognitive paradigm explores the mechanisms of gathering, processing and responding to the environment in a way that produces nonrandom movement in time and space and thus focuses on the navigation capacity of the individual. The optimality paradigm examines the relative efficacy of different movement strategies in optimizing some particular fitness currencies (e.g. energy gain or survival) over ecological or evolutionary timescales, and thus focuses mostly on the external factors affecting the internal state of the individual. The random paradigm analyzes the fit of observed animal tracks to various random walk models to assess, for example, search efficiency and thus focuses exclusively on the movement patterns. The movement ecology framework explicitly combines these basic components of movement and the links among them ( Fig.1) , which can be identified across all movement types and taxonomic groups . Thus, this framework offers a template for transdisciplinary integration of the four existing movement research paradigms ( Fig.1) to create jointly the new paradigm of movement ecology, devoted to the comprehensive study of all biological (whole-organism) movement phenomena. Movement ecology thus aims at unifying organismal movement research and aiding the development of a general theory of wholeorganism movements . To facilitate this unification, we need tools that can provide simultaneous information about the movement, energy expenditure and behavior of the studied organisms, and the environmental conditions they SummaryIntegrating biomechanics, behavior and ecology requires a mechanistic understanding of the processes producing the movement of animals. This calls for contemporaneous biomechanical, behavioral and environmental data along movement pathways. A recently formulated unifying movement ecology paradigm facilitates the integration of existing biomechanics, optimality, cognitive and random paradigms for studying movement. We focus on the use of tri-axial acceleration (ACC) data to identify behavioral modes of GPS-tracked free-ranging wild animals and demonstrate its application to study the movements of griffon vultures (Gyps fulvus, Hablizl 1783). In particular, we explore a selection of nonlinear and decision tree methods that include support vector mach...

show abstract

An easy, flexible solution to attach devices to hedgehogs (Erinaceus europaeus) enables long‐term high‐resolution studies

Barthel

Hofer

Berger

2018

Ecology and Evolution

View full text Add to dashboard Cite

Bio‐logging is an essential tool for the investigation of behavior, ecology, and physiology of wildlife. This burgeoning field enables the improvement of population monitoring and conservation efforts, particularly for small, elusive animals where data collection is difficult. Device attachment usually requires species‐specific solutions to ensure that data loggers exert minimal influence on the animal’s behavior and physiology, and ensure high reliability of data capture. External features or peculiar body shapes often make securing devices difficult for long‐term monitoring, as in the case with small spiny mammals. Here, we present a method that enables high‐resolution, long‐term investigations of European hedgehogs (Erinaceus europaeus) via GPS and acceleration loggers. We collected data from 17 wild hedgehogs with devices attached between 9 and 42 days. Our results showed that hedgehogs behaved naturally; as individuals curled, moved through dense vegetation, slipped under fences and built regular day nests without any indication of impediment. Our novel method makes it possible to not only attach high‐precision devices for substantially longer than previous efforts, but enables detachment and reattachment of devices to the same individual. This makes it possible to quickly respond to unforeseen events and exchange devices, and overcomes the issue of short battery life common to many lightweight loggers.

show abstract

Application testing of a new three-dimensional acceleration measuring system with wireless data transfer (WAS) for behavior analysis

Cited by 44 publications

References 5 publications

Observing the unwatchable through acceleration logging of animal behavior

Observing the unwatchable through acceleration logging of animal behavior

Using tri-axial acceleration data to identify behavioral modes of free-ranging animals: general concepts and tools illustrated for griffon vultures

An easy, flexible solution to attach devices to hedgehogs (Erinaceus europaeus) enables long‐term high‐resolution studies

Contact Info

Product

Resources

About