Abstract

Accelerometers in animal‐attached tags are powerful tools in behavioural ecology, they can be used to determine behaviour and provide proxies for movement‐based energy expenditure. Researchers are collecting and archiving data across systems, seasons and device types. However, using data repositories to draw ecological inference requires a good understanding of the error introduced according to sensor type and position on the study animal and protocols for error assessment and minimisation.Using laboratory trials, we examine the absolute accuracy of tri‐axial accelerometers and determine how inaccuracies impact measurements of dynamic body acceleration (DBA), a proxy for energy expenditure, in human participants. We then examine how tag type and placement affect the acceleration signal in birds, using pigeons Columba livia flying in a wind tunnel, with tags mounted simultaneously in two positions, and back‐ and tail‐mounted tags deployed on wild kittiwakes Rissa tridactyla. Finally, we present a case study where two generations of tag were deployed using different attachment procedures on red‐tailed tropicbirds Phaethon rubricauda foraging in different seasons.Bench tests showed that individual acceleration axes required a two‐level correction to eliminate measurement error. This resulted in DBA differences of up to 5% between calibrated and uncalibrated tags for humans walking at a range of speeds. Device position was associated with greater variation in DBA, with upper and lower back‐mounted tags varying by 9% in pigeons, and tail‐ and back‐mounted tags varying by 13% in kittiwakes. The tropicbird study highlighted the difficulties of attributing changes in signal amplitude to a single factor when confounding influences tend to covary, as DBA varied by 25% between seasons.Accelerometer accuracy, tag placement and attachment critically affect the signal amplitude and thereby the ability of the system to detect biologically meaningful phenomena. We propose a simple method to calibrate accelerometers that can be executed under field conditions. This should be used prior to deployments and archived with resulting data. We also suggest a way that researchers can assess accuracy in previously collected data, and caution that variable tag placement and attachment can increase sensor noise and even generate trends that have no biological meaning.

Highlights

  • Animal-­attached tags have revolutionised our understanding of wild animal ecology (Bograd et al, 2010; Sequeira et al, 2021; Yoda, 2019)

  • We examine the absolute accuracy of tri-­axial accelerometers and determine how inaccuracies impact measurements of dynamic body acceleration (DBA), a proxy for energy expenditure, in human participants

  • Accelerometer inaccuracies can result in errors in the raw acceleration of up to 5% per axis and, depending on the extent and direction of the errors across all three orthogonal axes, this can affect DBA metrics

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Summary

Introduction

Animal-­attached tags have revolutionised our understanding of wild animal ecology (Bograd et al, 2010; Sequeira et al, 2021; Yoda, 2019). Of the sensors often used, accelerometers (Yoda et al, 1999) are regarded as a powerful tool for studying wild animal behavioural ecology, with studies using them to look at the occurrence and intensity of behaviour (Chakravarty et al, 2019; Fehlmann et al, 2017), assess movement characteristics (Shepard et al, 2008) and as a proxy for energy expenditure (Wilson et al, 2020) The latter has developed rapidly since the demonstration that dynamic body acceleration (DBA) is related to energy expenditure across a range of vertebrates and invertebrates (Halsey et al, 2009; Wilson et al, 2006, 2019). The widespread availability and use of accelerometers mean that large datasets, collected over years, are available, providing valuable information about behaviour, including flight effort across temporal and spatial scales (Kranstauber et al, 2011) These data have been collected using different methods of attachment and by deploying a variety of different tags without critical analysis of the compatibility of different datasets (Sequeira et al, 2021)

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