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Animal lifestyle affects acceptable mass limits for attached tags


Wilson, Rory et al. (2021), Animal lifestyle affects acceptable mass limits for attached tags, Dryad, Dataset,


Animal-attached devices have transformed our understanding of vertebrate ecology. To minimize any associated harm, researchers have long advocated that tag masses should not exceed 3% of carrier body mass. However, this ignores tag forces resulting from animal movement. Using data from collar-attached accelerometers on 10 diverse free-ranging terrestrial species from koalas to cheetahs, we detail a tag-based acceleration method to clarify acceptable tag mass limits. We quantify animal athleticism in terms of fractions of animal movement time devoted to different collar-recorded accelerations and convert those accelerations to forces (acceleration × tag mass) to allow derivation of any defined force limits for specified fractions of any animal's active time. Specifying that tags should exert forces that are less than 3% of the gravitational force exerted on the animal's body for 95% of the time led to corrected tag masses that should constitute between 1.6% and 2.98% of carrier mass, depending on athleticism. Strikingly, in four carnivore species encompassing two orders of magnitude in mass ( ca 2–200 kg), forces exerted by ‘3%' tags were equivalent to 4–19% of carrier body mass during moving, with a maximum of 54% in a hunting cheetah. This fundamentally changes how acceptable tag mass limits should be determined by ethics bodies, irrespective of the force and time limits specified.


Tag deployments on free-ranging species

We selected 4 species of free-living carnivores for detailed analysis, exemplifying about 2 orders of magnitude of mass; 10 lions Panthera leo (mean mass ca. 152 kg), 1 cheetah Acinonyx jubatus (mass ca. 41 kg), 10 badgers Meles meles (mean mass ca. 9.1 kg) and 5 pine martens Martes martes (mean mass 1.9 kg), and fitted them with collar-mounted tri-axial accelerometers (‘Daily Diaries - Wildbyte Technologies []; measurement range 0-16 g [resolution 0.49 mg], recording frequency 40 Hz), all of which constituted less than 3% of the mass of the animal carriers (Table S1). Due to the weighting of the loggers, and more particularly their associated batteries, the units and sensors were positioned on the underside of the collar although during movement the collars could rotate, which could occasionally, temporarily bring the measuring system off the ventral position. After being equipped, the animals roamed freely, behaving normally, for periods ranging between 3 and 21 days before the devices were recovered. In addition to these, we also deployed collar-mounted accelerometers constituting <3% of the carrier mass (Table S1) on six select free-ranging animal species. We chose these species by capitalizing on available data from animals equipped with high temporal resolution acceleration tags on collars from different mammal families with varying lifestyles for comparison with the carnivores. The species and lifestyles were; a savannah-dwelling monkey - the olive baboon Papio Anubis (mean mass 15 kg), an arboreal herbivorous marsupial – the koala Phascolarctos cinereus (mean mass 10.3 kg), a nocturnal, semi-arboreal, herbivorous marsupial – the mountain brushtail possum Trichosurus cunninghami (mean mass 3.2 kg), a grass-eating, desert-dwelling bovid – the Arabian oryx Oryx leucoryx (mean mass 74 kg), a grass-eating, wood- and moor-dwelling cervid – the red deer Cervus elaphus (mean mass 135 kg) and a forest-dwelling, omnivorous pig – the wild boar Sus scrofa (mean mass 67 kg). Extensive details on species-specific tagging procedures are included in the Supplementary Materials.

Trials with domestic dogs

Twelve domestic dogs (Canis lupus domesticus) of seven different breed combinations and three main body types (small, racers and northern breeds), ranging 2-45 kg in body mass (Table S2), were volunteered by their owners and the RSPCA’s Llys Nini Wildlife Centre (Penllergaer, Wales) to take part in this study. Dog body masses were obtained from the most recent measurements taken by a veterinarian or the RSPCA and we measured body length, forelimb length and hindlimb length to the nearest cm. Two leather dog collars (short and long) of the same width were used to cover the range in dog neck size. Combinations of pre-prepared lead plates (up to 10 cm in length) and varying in mass (25, 35, 45, 50, 100, 150 and 175 g) were fashioned into collar loads equivalent to 1, 2 and 3% of each carrier dog’s body mass. The lead plates were stacked, the longest of them (for the greatest masses) being bent to replicate a 10 cm section of the collar circumference and attached securely to the ventral collar along their full-length using Tesa® tape. A tri-axial accelerometer and its supporting battery (3.2 V lithium ion) were taped securely to the load. The tag and battery combined weighed 11.9 g and, in the absence of any additional load, were considered negligible in mass and used as a control (0 % carrier body mass). All trials were approved by the Swansea University Animal Welfare Ethical Review Body (ethical approval number IP-1617-21D). Each dog was encouraged to traverse along a 25 m stretch of level, short-cut grass at slow (walk/amble), moderate (pace/trot) and fast (canter/gallop) speeds (because gait affects acceleration signatures substantially [24]) wearing collar-tags equivalent to 0, 1, 2 and 3% of their body mass (twelve gait and tag mass combinations) and trial order was randomized. Posts were spaced every 5 m along the track. A stopwatch was used to record the time taken (to the nearest s) for a dog to travel 20 m in order to calculate an average speed of travel (m s-1).

Data processing

In all cases of animals equipped with accelerometers, the 3 channels of raw acceleration data were converted to a single metric by calculating the vectorial sum of the acceleration following Vect sum = √(ax2+ay2+az2), where a is the instantaneous acceleration and the subscripts denote the different (orthogonally placed) acceleration axes. We chose to use the Vect sum rather than dynamic body acceleration metrics [26] because DBA values do not represent peak accelerations due to the gravity-based component being removed [27]. The specifics of the surge, heave and sway accelerations were not considered separately due to some collar roll. In the case of the free-living carnivores, we examined how travel gait affected the Vect sum by plotting the cumulative frequency distribution from each species during periods of walking, trotting and bounding. For the domestic dogs, we selected the maximum 4 peak accelerations in the Vect sum from the gait waveforms using the peak analysis tool in OriginLab (2020) to examine them as a function of average speed, gait, body mass and tag mass as a percentage of carrier body mass in the dogs. We standardized the use of four peaks because at the highest speeds some dogs only had four full waveforms during the test stretch. Gait was assessed visually in the dogs as a walk, trot or bound. The relative forces (% body mass) exerted by the tags on their animal carriers were calculated using F = ma, where m is the mass of the tag plus collar as a percentage of carrier mass and a is the acceleration (g).

Tag-based acceleration method (TbAM)

Finally, in a full cross-species comparison of the free-living animals, we plotted the cumulative frequency distribution of the Vect sum from each species during periods when they were active (by excluding periods where the acceleration signals were constant) to define the vector sum of the acceleration at species-specific 95% and 99% limits. 

Statistical analyses

Linear mixed-effects models were conducted in R Studio (version 4.0.3, [28]) within the ‘Lme4’ package (version 1.1-26) in order to investigate how the period between acceleration peaks, gait and body mass influenced peak accelerations across four species of wild carnivores, and separately in domestic dogs. Additionally, we investigated how travel speed (covariate), body mass (covariate), collar mass as a percentage of carrier body mass (covariate) and gait (fixed factor) influence peak accelerations and consequent forces exerted by the tags. Dog ID was included as a random factor in all models to accound for repeated measures. All potential interaction effects were first investigated and a step-wise back-deletion of non-significant interaction terms was conducted. Standard model diagnostics were conducted in order to ensure that model assumptions were met (examining q-q plots and plotting the residuals against fitted values) and data transformations were conducted in order to meet assumptions where appropriate. The F statistic and marginal and conditional R2 were determined using the ‘car (3.0-5)’ and ‘MuMIn (1.46.6)’ packages, respectively. Coefficients for best-fit lines in the figures were extracted from the final outputs of the models.


King Abdullah University of Science and Technology, Award: Sensor Initiative

Royal Society, Award: Wolfson Lab refurbishment scheme

National Trust and Forest Service NI, Award: Department of Learning and the Challenge Funding

King Abdulaziz University, Award: Vice Deanship of Research Chairs

The Royal Society, Award: 2009/R3 JP090604

Natural Environment Research Council, Award: NE/I002030/1

Department for Economy Global Challenges Research Fund

Department of Agriculture and Rural Development, Northern Ireland

Department for the Economy, Award: Studentship

National Science Foundation, Award: IIS-1514174

David and Lucile Packard Foundation, Award: 2016-65130 Fellowship

Alexander von Humboldt-Stiftung, Award: Alexander von Humboldt Professorship endowed by the Federal Ministry of Education and Research

Deakin University, Award: The advanced research supporting the forestry and wood‐processing sector's adaptation to global change

EVA4.0, Award: CZ.02.1.01/0.0/0.0/16_019/0000803

OPRDE, Award: QK1910462.

Department for Economy Global Challenges Research Fund

King Abdullah University of Science and Technology, Award: CAASE project under the KAUST Sensor Initiative

King Abdulaziz University, Award: Vice Deanship of Research Chairs

Packard Foundation Fellowship, Award: 2016-65130

Department of Agriculture and Rural Development, Northern Ireland, Award: studentships: DMS, NJM

Alexander von Humboldt Foundation, Award: Alexander von Humboldt Professorship endowed by the Federal Ministry of Education and Research

Department for the Economy studentship to JPT

Royal Society/Wolfson Lab refurbishment scheme

Advanced research supporting the forestry and wood-processing sector's adaptation to global change and the 4th industrial revolution

National Science Foundation, Award: IOS-1250895