Skip to main content
Download PDF
  • Telemetry case report
  • Open access
  • Published:

Best practice for collar deployment of tri-axial accelerometers on a terrestrial quadruped to provide accurate measurement of body acceleration

Animal Biotelemetry volume 8, Article number: 9 (2020) Cite this article

Abstract

Background

Tri-axial accelerometers are frequently deployed on terrestrial quadrupedal mammals using collars, because they are easy to fit and are thought to have minimal impact on the subject. Collar-attached devices are not fixed to the body and can move independently of the body. This may result in inaccurate measures of acceleration, reducing the accuracy of measured body movement. We determined the effect of collar size and collar weight on acceleration measured by a collar-mounted accelerometer on a quadruped mammal. The aim was to suggest best practice for sizes and weights of collars on which to deploy tri-axial accelerometers. Using pygmy goats, Capra aegagrus hircus, which were trained to walk at different speeds (0.8–3.0 km/h) on a treadmill, we measured body acceleration using a collar-mounted tri-axial accelerometer, with different collar sizes (individual neck circumference + 1 cm to + 9 cm) and collar weight (0.4% to 1.2% of individual weight).

Results

There was a significant effect of collar size, collar weight and walking speed on measured acceleration. Measured acceleration was less accurate and more variable when collars were looser and heavier. To measure body acceleration more accurately, we found that collar size should be within 5 cm or 16% of an individual’s neck circumference when it was heavy (up to 1.2% of animal’s body weight) or within 7 cm (33%) of neck circumference if the collar was light (up to 0.6% of animal body weight).

Conclusion

We suggest that not only reporting collar size and weight for welfare purposes, but it is also important to consider these aspects for scientific rigour, to ensure data are collected as accurately as possible. We provide guidelines for researchers fitting collar-attached devices to ensure a higher degree of accuracy of recorded body acceleration.

Introduction

Advances in biologging technology have led to the increased use of animal-borne tri-axial accelerometers to study domesticated and free-living animals [1,2,3]. This technology enables detailed information to be recorded about an individual’s orientation and body movement [4]. Devices typically record acceleration data at high frequencies (e.g. 20–50 Hz) in three axes which enables dynamic body acceleration to be calculated, such as vectoral dynamic body acceleration (VeDBA) [5, 6]. Measured acceleration can be used to identify patterns of behaviour through analyses of body movement and orientation [7,8,9,10]. Body acceleration has also been shown to correlate with oxygen consumption, and along with other movement-related metrics, can be used as an index of energy expenditure [11,12,13]. Furthermore, it can be included with other recorded data such as magnetic compass heading to infer movement paths of individuals by dead-reckoning [14].

The importance of correct attachment and positioning of biologging devices is broadly accepted, both for animal welfare considerations [15,16,17] and application purposes such as tag position to answer specific movement-related questions [5, 18]. Generally, devices are attached to the subject’s body, as close to the centre of mass as possible, and aligned so that the direction of measured axes is aligned with the organism’s anatomical axes [19]. Such measures are easier to apply when attaching devices directly to the animal’s hair or feathers using tape or glue, as is the case, for example, when attaching logging devices to marine mammals [20,21,22]. However, many studies utilise collar-attached systems, most commonly for deployment on quadrupedal terrestrial mammals [8, 23]. This moves the device away from the centre of mass, and unlike directly attached devices, collar-attached devices are not fixed to the body and may therefore move independently to the subject [19]. This may result in less accurate measurements of acceleration of the animal, and reduce the ability of accelerometry to determine behavioural patterns and body orientation.

Previous studies have proposed that in the interests of animal welfare, collar weight should not exceed 2% of body weight and collar size should be of appropriate tightness, dependent on the study species [17, 24]. A few studies have evaluated the effect of collar weight on the subjects’ behaviour [17, 25] and investigated the welfare consequences of collar tightness [26]. However, the effect of collar size and weight on acceleration measured by a collar-attached device has not yet been quantified. These determinations are important because device weight is related to battery size and, therefore, to the period over which a device can record. Researchers may only have one opportunity to deploy a device, and thus, it is important to ensure that collar size and weight are optimal for accurate measurement of body acceleration.

We used African pygmy goats (Capra aegagrus hircus) as a model quadruped species to measure body acceleration of walking, measured via VeDBA over a second, at different speeds on a treadmill, which acted as a controlled environment. The main aims of this study were (1) to assess the effect of different collar sizes, ranging from 3 to 10 cm larger than the subjects’ neck circumferences (S1–S5), and weights, ranging from 0.4 to 1.2% of the subjects’ body weights (W1–W5), on the measured acceleration of a collar-mounted device (Table 1); (2) to investigate the acceleration of a device acting as a control, positioned on the body of the animal using a harness so that the device is not subject to collar roll; and (3) to assess the effect of collar sizes and weights on the difference between acceleration measured on the collar and the harness. Selection amongst candidate general linear models (GLMs) used an information theoretic approach based on Aikake’s Information Criteria (corrected for small sample size) (AICc) [27]. Using predictions from the GLM output, we developed guidelines for relative collar sizes and weights, in order for future studies to measure body acceleration as accurately as possible.

Table 1 Each measured combination of collar size (S1–S5), ranging from 1 to 9 cm greater than the animals’ neck circumferences, and collar weight (W1–W5), ranging from 0.4 to 1.2% of the animals’ masses, for the different goats (G01–G04)
Full size table

Results

Effect of collar size and weight on VeDBA

The interaction between collar size and weight was retained in the model, positively affecting mean VeDBA (ΔAIC = 110.7 relative to the best model that excluded the interaction) with higher mean values and more variation recorded on looser and heavier collars (Fig. 1; Additional file 1). For heavy and looser collars (S5, W5), VeDBA was predicted to increase twice as fast as that for tighter and lighter collars (S1, W1 and W2). When the collar was loose but light (S5, W1 and W2), VeDBA was predicted to increase by more than two-thirds than if the collar was tight but heavy (S1, W5).

Fig. 1
figure 1

Mean VeDBA (g) as measured on the collar-attached device for each combination of collar size and weight: a collar weights with each collar size; b collar sizes with each collar weight. Boxes represent the mean and interquartile range, and the kernel density plots represent the probability density and the variance of the data

Full size image

Collar parameters had an important effect on the relationship between VeDBA and animal travel speed (Fig. 2; Table 2; Additional file 1). There was a positive relationship between speed and mean VeDBA recorded from the collar-attached device. There were also positive interactions between collar size and speed and collar weight and speed retained in the model. There was no interaction between speed and goat ID; however, goat ID was retained in the model.

Fig. 2
figure 2

The predicted relationship using the linear model between mean VeDBA (g) and walking speed for a different collar sizes and b different collar weights. Fitted lines represent the predicted relationship and error bars represent predicted standard error

Full size image
Table 2 The effect of collar size and weight on measured VeDBA, including the relationship between speed and goat ID as a fixed factor
Full size table

VeDBA measured by the harness

We investigated the relationship of harness-measured acceleration with the other variables to confirm that the relationship between acceleration and speed measured on the harness did not vary with different collar weights or sizes. There was a positive relationship between speed and VeDBA measured using the harness-attached device (ΔAIC = 578.6 relative to the best model that excluded the interaction; Table 2) and goat ID was retained in the model. The effects of collar size and weight were retained. However, the interactions between speed and collar weight, or speed and collar size were not retained in the model (ΔAIC = 3.3 and ΔAIC = 1.1, respectively, relative to the best model that excluded the interaction). Showing that despite retaining collar size and weight in the model, it did not influence the relationship between acceleration and speed.

Difference between collar and harness

The interaction between collar size and collar weight was retained in the model, positively affecting difVeDBA (the difference between collar- and harness-measured acceleration), with a larger difference and more variation recorded when the collar was looser and heavier (ΔAIC = 8.99 relative to the best model that excluded the interaction; Table 2; Fig. 3). When collar size increased, the predicted increase in difVeDBA (g) per km/h was up to four times greater for heavier collars than for lighter collars, and when collar weight increased, the predicted increase was up to three times larger for looser collars than tighter collars (Table 3; Additional file 1).

Fig. 3
figure 3

The predicted relationship using the model output between the differences in mean difVeDBA (g), calculated as the difference between VeDBA measured on the collar and the harness, and animal walking speed for a collar sizes and b collar weights. Fitted lines represent the predicted relationship and error bars the predicted standard errors

Full size image
Table 3 Predicted measured increase difVeDBA (g) per km/h for each collar size (S1–S5) and weight (W1–W5) combination with predicted standard error
Full size table

The extent of agreement between collar- and harness-derived estimates of VeDBA was negatively affected by animal travel speed as difVeDBA increased with speed (ΔAIC = 260.1 relative to the best model that excluded the interaction). There were significant interactions between speed and collar size (Fig. 3a) and speed and collar weight (Fig. 3b) on measured VeDBA. There was also a significant interaction between goat ID and speed.

Discussion

Our results indicate that the size and weight of a collar are likely to affect acceleration measured in animal-borne tags. To ensure ethical deployment of collars onto subject animals, researchers must demonstrate that the best available techniques are being used that do not cause harm [16]. Therefore, in the context of animal welfare, it is also important to consider the effect of collar size and weight on the ability to measure the subject’s body movement accurately.

Collars that are too tight may cause discomfort or injury and not account for potential animal growth, whilst collars that are too loose may ensnare the animal or be prematurely removed [28]. In line with ethical recommendations, we did not test collar sizes that are predicted to cause the animal discomfort [15]. Our results show, however, that the size and weight of the collar have a significant effect on the measured acceleration when the subject is walking. Tighter and lighter collars are more likely to provide more accurate measures of body movement, with smaller differences in effect size and variation between collar sizes S1 to S3 and collar weights W1 to W3. These collar parameters are also more likely to fit with ethical constraints. In contrast, looser and heavier collars are more likely to yield larger acceleration values and variation, reducing accuracy of measured acceleration. Collar sizes S4 and S5 with collar weight W5 were likely to have an effect size five times larger than the collar combinations S1/2 and W1/2.

This study also measured the interaction between collar size and weight. We found that if the collar is fitted tightly (neck + 5 cm or 16% of neck circumference), collar weight will have minimal effects on measured acceleration, see Table 2. As the increase in VeDBA is larger for heavier collars, it is recommended that when deploying collars heavier than 0.8% of body weight, collar size should be kept to within 5 cm or 16% of the subject’s neck size. If the collar is looser (> neck + 5 cm), then weight will have a larger effect; therefore, if this size is used, researchers should consider using collars within 0.6% of the subject’s body weight. The fact that the effect of collar size was smaller than that of weight demonstrates that size is important to consider but, within 16% of neck circumference, is less likely to affect measured acceleration significantly. Changes in body mass must be considered when deploying collars, as many ungulates undergo substantial body mass changes seasonally, affecting collar tightness with the potential to cause harm to the subject [26, 29]. Therefore, collars should be deployed that are neither too tight nor too loose in proportion to the neck size of the subject to satisfy ethics and provide accurate measurement of relative body acceleration. Of course, this study does not address body mass changes; considerations in relation to this must be made for any given study species.

This study measured the effect of collar weight within 1.2% of the subject’s body weight. The commonly suggested ‘5% (or 3%) rule’ has been suggested to be arbitrary [24]. However, studies investigating the effect of logger weight have mainly focused on flying animals [30, 31]. On terrestrial mammals, it has been suggested that 2% is more appropriate for collar-attached devices [15], and even an increase from 0.4 to 0.6% in collar weight has been shown to affect the behaviour of a large ungulate [25]. This study shows that when using collar-attached devices to measure body movement, weight has an effect on measured acceleration. It is important to consider device weight for both measurement accuracy as well as considering its potential influence on behaviour.

The effect of collar size and weight also varied with walking speed: at low speeds, there was little variation between collar sizes and weights, but at higher speeds, variation increased [19]. Wild animals are likely to display a wider range of behaviours and larger accelerations than in our controlled experimental setting, depending on the species’ body size and characteristics of their body movement [10, 23]. The larger variation in these behaviours may be exaggerated by collars that are loose and/or heavy; therefore, in a normal setting, the observed effects of collar size and weight might be larger and more variable. It is, therefore, important to consider that this effect may be different in behaviours other than walking. Furthermore, when applying these recommendations to other species, it is important to consider other factors such as body shape and differences in movement, making deployment decisions based on the study species in question.

Conclusions

This study highlights that both the size and weight of the collar affect measured body acceleration in a quadrupedal terrestrial mammal. The importance of correctly fitting a collar for deployment of devices that record body movement, to ensure the highest standards of both welfare and scientific precision, is highlighted. We recommend that collar tightness be kept to + 16% of neck size and collar weight to be kept within 0.8% of the subjects’ body mass. If the collar is tighter than this + 16% guideline, collar weight will have less effect, and thus, heavier collars will not have a negative effect. We suggest consideration of these recommendations when deploying collar-mounted devices and that researchers should report the size and weight in research outputs to demonstrate best practise and enable similar studies to employ comparable and reproducible methods.

Methods

Study animals

The study took place in June 2018 at Belfast zoo, within the subjects’ normal housing area. Four female adult African pygmy goats were used (body weight: 13.5–23.1 kg; Table 4), as they are easily trained domesticated caprids amenable to the use of collars and harnesses. Using positive reinforcement, consisting of the subject’s normal daily food, individuals were trained to walk on a treadmill. Individuals were habituated to the equipment and then trained in stages over a period of 1 month. Each individual walked on a treadmill for a period of up to 10 min, at different speeds, wearing both a collar (Dog collar, Pets at Home, UK; 53 g) and a harness (Adjustable nylon dog harness, Bunty Pet Products, UK; 128 g).

Table 4 Details of each individual goat (G01–G04) used in the study, including body weight, neck size and age
Full size table

Acceleration data collection

We used tri-axial accelerometers (Daily Diary tag, Wildbyte Technologies; [19]) that measured acceleration in three orthogonal axes at a sampling rate of 40 Hz. The devices were powered by a Li-Po rechargeable battery (Cameron Sino, HK, 31.54 × 25.13 × 3.95 mm; 5.5 g), enclosed in plastic casing (35 × 45 × 12 mm; combined mass 15.2 g) and attached using waterproof tape (Tesa® tape 4651, Tesa, Germany). One accelerometer was placed on the collar in the ventral position on the subject’s neck (Fig. 4); using different combinations of collar sizes, from approximately 3 to 30% (+ 1 to + 9 cm) larger than individuals’ neck sizes, and weights, from 0.4 to 1.2% (88 to 285 g) of individuals’ body masses (Table 4). Weight was added adjacent to the accelerometer using metal nuts taped to the collar, with the aim of resembling larger battery weight. A second accelerometer was placed on the harness in a ventral position, to act as a control in the same position as the first device. The harness provided a more rigid placement, minimising movement of the accelerometer beyond that of the study animal (Fig. 4). Collar combinations measured were each collar size with the smallest and largest weight, and each collar weight with the tightest and loosest collar (Table 1). After each individual was trained and acclimated to the equipment, each collar combination was tested on each individual once. Collar combinations that tested the largest and smallest collar size with the lightest and heaviest collar weight were measured. Goats walked at speeds between 0.8 and 3.0 km/h at increments of 0.2 km/h and walking speeds were recorded in a random order, for a minimum duration of 1 min. Each individual was video-recorded during data collection to remove data obtained during behaviours other than walking (Fujifilm XP120, Fujifilm, Japan; 59.94 frame/s). Sections, where the speed of the treadmill was changing (~ 15 s), were also removed.

Fig. 4
figure 4

Goat (G03) with an accelerometer attached to the collar (a) and to the harness (b)

Full size image

Data were processed and analysed in R version 3.6.2 [32]. Data were labelled for the corresponding speed, collar size and collar weight. To calculate VeDBA, raw acceleration for each axis was used to derive static acceleration over a running mean of 2 s and thus the vectoral sum of dynamic body acceleration was calculated (VeDBA) [33]. Mean VeDBA measured over a 1s interval was calculated for the collar- and harness-attached devices. To determine how acceleration measured by the collar changes, the difference between the collar and harness attached devices was also calculated \(\left( {{\text{difVeDBA}} = \overline{{ {\text{VeDBA}}}}_{{{\text{collar}} }} - \overline{\text{VeDBA}}_{\text{harness}} } \right)\).

Data analysis

To understand the relationship between collar size and weight on measured acceleration (VeDBA), three separate general linear fixed effect models were used to (1) investigate the VeDBA measured on the collar-attached device; (2) investigate the VeDBA measured on the harness-attached device; and (3) investigate the relationship with the difference in VeDBA measured from the two devices (difVeDBA). The fixed variables included in the models were goat ID, walking speed and collar size and weight as categorical variables. A two-way interaction between collar size and weight was included. The two-way interactions between speed and the variables, goat ID, collar size and collar weight, were also included. The global models were simplified using the “dredge” function in the R package MuMin [34] which uses AICc to assess the best fit model [27]. Models within ΔAICc ≤ 6 were retained for inference and the simplest model was selected (Table 2 and Additional file 1) [35]. Model residuals were checked for a normal distribution.

Availability of data and materials

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

VeDBA:

Vectoral Dynamic Body Acceleration

difVeDBA:

Difference of VeDBA measured on collar and harness

GLM:

General Linear Model

AICc :

Akaike Information Criteria

References

  1. Chmura HE, Glass TW, Williams CT. Biologging physiological and ecological responses to climatic variation: new tools for the climate change era. Front Ecol Evol. 2018;6:1–9.

    Article  Google Scholar 

  2. Williams HJ, Taylor LA, Benhamou S, Bijleveld AI, Clay TA, De Grissac S, et al. Optimising the use of bio-loggers for movement ecology research. J Anim Ecol. 2020;89:189–206.

    Article  Google Scholar 

  3. Hughey LF, Hein AM, Strandburg-Peshkin A, Jensen FH. Challenges and solutions for studying collective animal behaviour in the wild. Phil Trans R Soc B Biol Sci. 2018;373:1746.

    Article  Google Scholar 

  4. Brown DD, Kays R, Wikelski M, Wilson RP, Klimley A. Observing the unwatchable through acceleration logging of animal behavior. Anim Biotelemetry. 2013;1:20.

    Article  Google Scholar 

  5. Shepard ELC, Wilson RP, Halsey LG, Quintana F, Laich AG, Liebsch N, et al. Derivation of body motion via appropriate smoothing of acceleration data. Aquat Biol. 2008;10:47–60.

    Google Scholar 

  6. Williams HJ, Holton MD, Shepard ELC, Largey N, Norman B, Ryan PG, et al. Identification of animal movement patterns using tri-axial magnetometry. Mov Ecol. 2017;5:6.

    Article  Google Scholar 

  7. Alvarenga FAP, Borges I, Palkovic L, Rodina J, Oddy VH, Dobos RC. Using a three-axis accelerometer to identify and classify sheep behaviour at pasture. Appl Anim Behav Sci. 2016;181:91–9.

    Article  Google Scholar 

  8. Pagano AM, Rode KD, Cutting A, Owen MA, Jensen S, Ware JV, et al. Using tri-axial accelerometers to identify wild polar bear behaviors. Endanger Species Res. 2017;32:19–33.

    Article  Google Scholar 

  9. Tatler J, Cassey P, Prowse TAA. High accuracy at low frequency: detailed behavioural classification from accelerometer data. J Exp Biol. 2018;221:jeb184085.

    Article  Google Scholar 

  10. Fehlmann G, O’Riain MJ, Hopkins PW, O’Sullivan J, Holton MD, Shepard ELC, King AJ. Identification of behaviours from accelerometer data in a wild social primate. Anim Biotelemetry. 2017;5:1–11.

    Article  Google Scholar 

  11. Halsey LG, Shepard ELC, Quintana F, Gomez Laich A, Green JA, Wilson RP. The relationship between oxygen consumption and body acceleration in a range of species. Comp Biochem Physiol. 2009;152:197–202.

    Article  CAS  Google Scholar 

  12. Gleiss AC, Wilson RP, Shepard ELC. Making overall dynamic body acceleration work: on the theory of acceleration as a proxy for energy expenditure. Methods Ecol Evol. 2011;2:23–33.

    Article  Google Scholar 

  13. Wilson RP, Börger L, Holton MD, Scantlebury DM, Gómez-Laich A, Quintana F, et al. Estimates for energy expenditure in free-living animals using acceleration proxies: a reappraisal. J Anim Ecol. 2019;80:161-72.

    Google Scholar 

  14. Bidder OR, Walker JS, Jones MW, Holton MD, Urge P, Scantlebury DM, et al. Step by step: reconstruction of terrestrial animal movement paths by dead-reckoning. Mov Ecol. 2015;3:23.

    Article  CAS  Google Scholar 

  15. Hawkins P. Bio-logging and animal welfare: practical refinements. Mem Natl Polar Res Inst. 2004;58:58–68.

    Google Scholar 

  16. Wilson RP, McMahon CR. Measuring devices on wild animals: what constitutes acceptable practice? Front Ecol Environ. 2006;4:147–54.

    Article  Google Scholar 

  17. Coughlin CE, Van Heezik Y. Weighed down by science: do collar-mounted devices affect domestic cat behaviour and movement? Wildl Res. 2014;41:606–14.

    Article  Google Scholar 

  18. Shillinger GL, Bailey H, Bograd SJ, Hazen EL, Hamann M, Gaspar P, et al. Tagging through the stages: technical and ecological challenges in observing life histories through biologging. Mar Ecol Prog Ser. 2018;457:165–70.

    Article  Google Scholar 

  19. Wilson RP, Shepard ELC, Liebsch N. Prying into the intimate details of animal lives: use of a daily diary on animals. Endanger Species Res. 2008;4:123–37.

    Article  Google Scholar 

  20. Bullock RW, Guttridge TL, Cowx IG, Elliott M. The behaviour and recovery of juvenile lemon sharks Negaprion brevirostris in response to external accelerometer tag attachment. J Fish Biol. 2015;87:1342–54.

    Article  CAS  Google Scholar 

  21. Graf PM, Wilson RP, Qasem L, Hackländer K, Rosell F. The use of acceleration to code for animal behaviours; a case study in free-ranging Eurasian beavers Castor fiber. PLoS ONE. 2015;10:1–17.

    Google Scholar 

  22. Horning M, Andrews RD, Bishop AM, Boveng PL, Costa DP, Crocker DE, et al. Best practice recommendations for the use of external telemetry devices on pinnipeds. Anim Biotelemetry. 2019;7:1–17.

    Article  Google Scholar 

  23. Wang Y, Nickel B, Rutishauser M, Bryce CM, Williams TM, Elkaim G, Wilmers CC. Movement, resting, and attack behaviors of wild pumas are revealed by tri-axial accelerometer measurements. Mov Ecol. 2015;3:2.

    Article  Google Scholar 

  24. Casper RM. Guidelines for the instrumentation of wild birds and mammals. Anim Behav. 2009;78:1477–83.

    Article  Google Scholar 

  25. Brooks C, Bonyongo C, Harris S. Effects of global positioning system collar weight on zebra behavior and location error. J Wildl Manag. 2008;72:527–34.

    Article  Google Scholar 

  26. Krausman PR, Bleich VC, Cain JW, Stephenson TR, DeYoung DW, McGrath PW, et al. From the field: neck lesions in ungulates from collars incorporating satellite technology. Wildl Soc Bull. 2004;32:987–91.

    Article  Google Scholar 

  27. Burnham KP, Anderson DR. Model selection and multimodel inference: a practical information-theoretic approach. New York: Springer; 2002.

    Google Scholar 

  28. Hellgren EC, Carney DW, Garner NP, Vaughan MR. Use of breakaway cotton spacers on radio collars. Wildl Soc Bull. 1988;16:216–8.

    Google Scholar 

  29. Gedir JV. A non-invasive system for remotely monitoring heart rate in free-ranging ungulates. Anim Welf. 2001;10:81–9.

    Google Scholar 

  30. Aldridge HDJN, Brigham RM. Load carrying and maneuverability in an insectivorous bat: a test of the 5% “rule” of radio-telemetry. J Mammal. 1988;69:379–82.

    Article  Google Scholar 

  31. Phillips RA, Xavier JC, Croxall JP. Effects of satellite transmitters on albatrosses and petrels. Auk. 2003;120:1082–90.

    Article  Google Scholar 

  32. R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2019. https://www.r-project.org/.

  33. Qasem L, Cardew A, Wilson A, Griffiths I, Halsey LG, Shepard ELC, et al. Tri-axial dynamic acceleration as a proxy for animal energy expenditure; should we be summing values or calculating the vector? PLoS ONE. 2012;7:e31187.

    Article  CAS  Google Scholar 

  34. Barton K. MuMIn: multi-model inference. R package version 1.42.1. 2018. https://CRAN.R-project.org/package=MuMIn.

  35. Richards SA, Whittingham MJ, Stephens PA. Model selection and model averaging in behavioural ecology: the utility of the IT-AIC framework. Behav Ecol Sociobiol. 2011;65:77–89.

    Article  Google Scholar 

Download references

Acknowledgements

We thank Belfast Zoo and Belfast City Council for allowing us to conduct this study, and the staff at Belfast Zoo for their help during data collection.

Funding

ED was supported by a Department for Education studentship, Northern Ireland.

Author information

Authors and Affiliations

  1. School of Biological Sciences, Queen’s University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, Northern Ireland, UK

    Eleanor R. Dickinson, Nikki J. Marks & David M. Scantlebury

  2. Department of Biosciences, Durham University, South Road, Durham, DH1 3LE, England, UK

    Philip A. Stephens

  3. Biosciences, College of Science, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales, UK

    Rory P. Wilson

Authors
  1. Eleanor R. Dickinson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Philip A. Stephens
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nikki J. Marks
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rory P. Wilson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. David M. Scantlebury
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

ED, RPW and DMS conceived the study design. ED collected the data. ED and PS analysed the data. ED drafted the manuscript. All authors provided comments. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Eleanor R. Dickinson.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the Queens University Belfast ethics committee (QUB-BS-AREC-19-004) and Belfast Zoo.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Additional file 1.

The estimated coefficients of variables for each general linear model used: (1) effect of collar size and weight on VeDBA, (2) VeDBA measured by the harness and (3) difference between collar and harness. Bold font indicates that 95% confidence intervals for coefficients did not overlap zero.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dickinson, E.R., Stephens, P.A., Marks, N.J. et al. Best practice for collar deployment of tri-axial accelerometers on a terrestrial quadruped to provide accurate measurement of body acceleration. Anim Biotelemetry 8, 9 (2020). https://doi.org/10.1186/s40317-020-00198-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40317-020-00198-9

Keywords

Animal Biotelemetry

ISSN: 2050-3385