Integration, Calibration, and Experimental Verification of a Speed Sensor for Swimming Animals

Gabaldon, Joaquin; Turner, Ellen L.; Johnson-Roberson, Matthew; Barton, Kira; Johnson, Mark; Anderson, Erik J.; Shorter, K. Alex

doi:10.1109/jsen.2019.2895806

Cited by 18 publications

(12 citation statements)

References 32 publications

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“…This is likely because speed can be more easily measured or approximated in water, with previous studies obtaining estimates via acoustic flow noise [e.g., 54], passive sonar [e.g., 55], pitch and change in depth [e.g., 8] and speed sensors [cf. 13,56,57]. The efficacy of such techniques diminishes within the aerial environment, principally, due to the marked difference between water and air density [cf.…”

Section: Introductionmentioning

confidence: 99%

Dead-Reckoning Animal Movements in R – A Reappraisal Using Gundog.Tracks

Gunner

Holton

Scantlebury

et al. 2021

Preprint

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Background Fine-scale data on animal position are increasingly enabling us to understand the details of animal movement ecology and dead-reckoning, a technique integrating motion sensor-derived information on heading and speed, can be used to reconstruct fine-scale movement paths at sub-second resolution, irrespective of the environment. On its own however, the dead-reckoning process is prone to cumulative errors, so that position estimates quickly become uncoupled from true location. Periodic ground-truthing with aligned location data (e.g., from global positioning technology) can correct for this drift between Verified Positions (VPs). Yet relatively few bio-logging studies have adopted this approach due to an apparent inaccessibility of the complex analytical processes involved. We present step-by-step instructions for implementing Verified Position Correction (VPC) dead-reckoning in R using the tilt-compensated compass method, accompanied by the mathematical protocols underlying the code and improvements and extensions of this technique to reduce the trade-off between VPC rate and dead-reckoning accuracy. These protocols are all built into a user-friendly, fully-annotated VPC dead-reckoning R function; Gundog.Tracks, with multi-functionality to reconstruct animal movement paths across terrestrial, aquatic, and aerial systems, provided within the supplementary information as well as online (GitHub). Results The Gundog.Tracks function is demonstrated on three contrasting model species (the African lion Panthera leo, the Magellanic penguin Spheniscus magellanicus, and the Imperial cormorant Leucocarbo atriceps) moving on land, in water and in air, respectively. We show the effect of uncorrected errors in speed estimations, heading inaccuracies and infrequent VPC rate and demonstrate how these issues can be addressed. Conclusions The function provided will allow anyone familiar with R to dead-reckon animal tracks readily and accurately, as the key complex issues are dealt with by Gundog.Tracks. This will help the community to consider and implement a valuable, but often overlooked method of reconstructing high-resolution animal movement paths across diverse species and systems without requiring a bespoke application.

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Section: Introductionmentioning

confidence: 99%

Dead-Reckoning Animal Movements in R – A Reappraisal Using Gundog.Tracks

Gunner

Holton

Scantlebury

et al. 2021

Preprint

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“…To measure speed through water, the revolution rate of the micro-turbine sensor on the MTag was converted to speed through a linear calibration (slope-intercept). The calibration and verification for this speed sensor configuration is detailed in Gabaldon et al [ 51 ]. Measurement of speed through water was numerically integrated to estimate total distance traveled by the animal while wearing the tag.…”

Section: Methodsmentioning

confidence: 99%

Bottlenose dolphin habitat and management factors related to activity and distance traveled in zoos and aquariums

et al. 2021

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High-resolution non-invasive cetacean tagging systems can be used to investigate the influence of habitat characteristics and management factors on behavior by quantifying activity levels and distance traveled by bottlenose dolphins (Tursiops truncatus and Tursiops aduncus) in accredited zoos and aquariums. Movement Tags (MTags), a bio-logging device, were used to record a suite of kinematic and environmental information outside of formal training sessions as part of a larger study titled “Towards understanding the welfare of cetaceans in zoos and aquariums” (colloquially called the Cetacean Welfare Study). The purpose of the present study was to explore if and how habitat characteristics, environmental enrichment programs, and training programs were related to the distance traveled and energy expenditure of dolphins in accredited zoos and aquariums. Bottlenose dolphins in accredited zoos and aquariums wore MTags one day per week for two five-week data collection periods. Overall dynamic body acceleration (ODBA), a proxy for energy expenditure, and average distance traveled per hour (ADT) of 60 dolphins in 31 habitats were examined in relation to demographic, habitat, and management factors. Participating facilities were accredited by the Alliance for Marine Mammal Parks and/or Aquariums and the Association of Zoos & Aquariums. Two factors were found to be related to ADT while six factors were associated with ODBA. The results showed that enrichment programs were strongly related to both ODBA and ADT. Scheduling predictable training session times was also positively associated with ADT. The findings suggested that habitat characteristics had a relatively weak association with ODBA and were not related to ADT. In combination, the results suggested that management practices were more strongly related to activity levels than habitat characteristics.

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“…For applications where animal speed is used to test ecological and physiological hypotheses, many biologging tags have introduced external sensors to measure passing flow. Many have calibrated measured speed with the rotation rate of external impellers (Blackwell, Haverl, Le Boeuf, & Costa, 1999;Burgess, Tyack, Le Boeuf, & Costa, 1998;Watanabe, Lydersen, Fisk, & Kovacs, 2012) and micro-turbines (Gabaldon et al, 2019), as well as the amplitude of vibrations as measured by the tag's accelerometers (Cade, Barr, Calambokidis, Friedlaender, & Goldbogen, 2018). Absolute speed estimates are sensitive to stalling at high and low speeds, tag shape and placement on the body, orientation with respect to the flow and calibration technique, as well as to the estimation errors in the analytical techniques (e.g.…”

Section: Speed Estimationmentioning

confidence: 99%

Foraging rates of ram‐filtering North Atlantic right whales

et al. 2019

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North Atlantic right whales spend their summer months foraging primarily in American and Canadian Atlantic waters on high‐energy‐density prey. Here, they rapidly accumulate and store energy obtained within a few months to support future migrations and reproduction while fasting. High drag from their ram‐filter foraging strategy places a limit on what prey densities will be energetically efficient to target. Our understanding of the volume of prey‐laden water filtered by right whales during a dive or foraging bout, and what information they use to decide to forage or not, has been limited by the difficulties of measuring when they feed at depth, how fast they swim during continuous ram filtration and how often they might swallow accumulated prey. We used 10 DTAG deployments from right whales in the Bay of Fundy, Canada, to quantify swimming speeds and estimate the volume of prey‐laden water filtered per dive. We used the tag's inertial sensors to evaluate the timing of frequent biomechanical changes that likely indicate the truncation of continuous filtration, and whether the number or timing of these fluking bouts relates to longer feeding dives or other foraging decisions. During foraging dives, right whales descended at 1.4 (±0.2 SD) m/s and slowed to swim at 1.1 (±0.3) m/s while filtering. We found consistent pauses in the fluking behaviour of foraging right whales, every 56 (±22) s. Whales filtered on average 78 (±30) m3 of water per fluking bout and on average 673 (±201) m3 per dive. Right whales filter large volumes of water at low speeds with a high duty cycle, but require sufficiently high prey energy densities to compensate for a high‐drag foraging strategy. Closely related bowhead whales have a larger gape but swim more slowly, filtering greater volumes with lower drag. Our findings highlight that right whales acquire their energy in a relatively short period of intense foraging; even moderate changes in their feeding behaviour or their prey energy density are likely to negatively impact their yearly energy budgets and therefore reduce fitness substantially. A plain language summary is available for this article.

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Integration, Calibration, and Experimental Verification of a Speed Sensor for Swimming Animals

Cited by 18 publications

References 32 publications

Dead-Reckoning Animal Movements in R – A Reappraisal Using Gundog.Tracks

Dead-Reckoning Animal Movements in R – A Reappraisal Using Gundog.Tracks

Bottlenose dolphin habitat and management factors related to activity and distance traveled in zoos and aquariums

Foraging rates of ram‐filtering North Atlantic right whales

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