Continuous on‐board behaviour classification using accelerometry: A case study with a new <scp>GPS‐3G‐Bluetooth</scp> system in Pacific black ducks

Yu, Hui; Deng, Jian; Leen, Trent; Li, Guozheng; Klaassen, Marcel

doi:10.1111/2041-210x.13878

Cited by 13 publications

(11 citation statements)

References 22 publications

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“…Details on the properties and functioning of the GPS-3G-Bluetooth trackers, including the on-board ACC data processing method and performance, as well as the deployment of the trackers on the six Pacific Black Ducks can be found in Yu, Deng [ 19 ]. In brief, the trackers logged GPS fixes at hourly intervals when battery levels allowed (i.e.…”

Section: Methodsmentioning

confidence: 99%

“…Korpela et al (2020) used on-board behaviour classification through ACC data to control data sampling of the more energy consuming on-board camera to extend the runtimes of field experiments. Recently, we developed a tracking system [ 19 ] using on-board continuous behaviour classification from ACC data that proved to be energy-, weight- and cost-efficient and allowed for continuous recording of behaviour that could be transmitted through the 3G mobile network on a daily basis.…”

Section: Introductionmentioning

confidence: 99%

“…Our previous work [ 19 ] focused on describing the functioning and technical performance of the new tracking system using on-board and continuous behaviour classification in six free-ranging Pacific black ducks Anas superciliosa . Here, using the same data set, we focus on how this system can enhance our ecological understanding of animals when compared to systems that lack this functionality (i.e.…”

Section: Introductionmentioning

confidence: 99%

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Increasingly detailed insights in animal behaviours using continuous on-board processing of accelerometer data

Klaassen²,

Deng³

et al. 2022

Mov Ecol

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Background: Studies of animal behaviour, ecology and physiology are continuously benefitting from progressing biologging techniques, including the collection of accelerometer data to infer animal behaviours and energy expenditure. In one of the most recent technological advances in this space, on-board processing of raw accelerometer data into animal behaviours proves highly energy-, weight- and cost-efficient allowing for continuous behavioural data collection in addition to regular positional data in a wide range of animal tracking studies. Methods: We implemented this latest development in collecting continuous behaviour records from 6 Pacific Black Ducks Anas superciliosa to evaluate some of this novel technique’s potential advantages over tracking studies lacking behavioural data or recording accelerometer data intermittently only. We (i) compared the discrepancy of time-activity budgets between continuous records and behaviours sampled with different intervals, (ii) compared total daily distance flown using hourly GPS fixes with and without additional behavioural data and (iii) explored how behaviour records can provide additional insights for animal home range studies. Results: Using a total of 690 days of behaviour records across six individual ducks distinguishing eight different behaviours, we illustrated the improvement that is obtained in time-activity budget accuracy if continuous rather than interval-sampled accelerometer data is used. Notably, for rare behaviours such as flying and running, error ratios > 1 were common when sampling intervals exceeded 10 min. Using 72 days of hourly GPS fixes in combination with continuous behaviour records over the same period in one individual duck, we showed behaviour-based daily distance estimation is significantly higher (up to 540%) than the distance calculated from hourly sampled GPS fixes. Also, with the same 72 days of data for one individual duck, we showed how this individual used specific sites within its entire home range to satisfy specific needs (e.g. roosting and foraging). Conclusion: We showed that by using trackers allowing for continuous recording of animal behaviour, substantial improvements in the estimation of time-activity budgets and daily traveling distances can be made. With integrating behaviour into home-range estimation we also highlight that this novel tracking technique may not only improve estimations but also open new avenues in animal behaviour research, importantly improving our knowledge of an animal’s state while it is roaming the landscape.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Increasingly detailed insights in animal behaviours using continuous on-board processing of accelerometer data

Klaassen²,

Deng³

et al. 2022

Mov Ecol

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show abstract

“…Details on the properties and functioning of the GPS-3G-Bluetooth trackers, including the on-board ACC data processing method and performance, as well as the deployment of the trackers on the six Paci c Black Ducks can be found in Yu, Deng (25). In brief, the trackers logged GPS xes at hourly intervals when battery levels allowed (i.e., >= 3.7v).…”

Section: Duck Trackingmentioning

confidence: 99%

“…Korpela et al (2020) used on-board behaviour classi cation through ACC data to control data sampling of the more energy consuming on-board camera to extend the runtimes of eld experiments. A very recent developed tracking system (25) proved that on-board continuous behaviour classi cation from ACC data was energy, weight and cost e cient and allowed for continuous recording of behaviour that could be transmitted through the 3G mobile network on a daily basis.…”

Section: Introductionmentioning

confidence: 99%

Revealing increasingly detailed insights in animal behaviours using continuous on-board processing of accelerometer data

Deng²,

Leen

et al. 2022

Preprint

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Background Studies of animal behaviour, ecology and physiology are continuously benefitting from progressing biologging techniques, including the collection of accelerometer data to infer animal behaviours and energy expenditures. In one of the most recent technological advances in this space, on-board processing of raw accelerometer data into animal behaviours proofed highly energy, weight and cost efficient allowing for continuous behavioural data collection in addition to regular positional data in a wide range of animal tracking studies. Methods We implemented this latest development in collecting continuous behaviour records from 6 Pacific Black Ducks Anas superciliosa to evaluate some of this novel technique’s potential advantages over tracking studies lacking behavioural data or recording accelerometer data intermittently only. We (i) compared the discrepancy of time-activity budgets between continuous records and behaviours sampled with different intervals, (ii) compared total daily distance flown using hourly GPS fixes with and without additional behavioural data and (iii) explored how behaviour records can provide additional insights for animal home range studies. Results Using a total 591 days of behaviour records across six individual ducks distinguishing eight different behaviours, we illustrated the improvement that is obtained in time-activity budget accuracy if continuous rather than interval-sampled accelerometer data is used. Notably for rare behaviours such as flying and running, error ratios > 1 were common when sampling intervals exceeded 10 minutes. Using 72 days of hourly GPS fixes in combination with continuous behaviour records over the same period in one individual duck, we showed behaviour-based daily distance estimation is significantly higher (up to 540%) than the distance calculated from hourly sampled GPS fixes. Also, with the same 72 days of data for one individual duck, we showed how this individual used specific sites within its entire home range to satisfy specific needs (e.g., roosting and foraging). Conclusions We showed that by using trackers allowing for continuous recording of animal behaviour, substantial improvements in the estimation of time-activity budgets and daily traveling distances can be made. With integrating behaviour into home-range estimation we also highlight that this novel tracking technique may not only improve estimations but also open new avenues in animal behaviour research, importantly improving our knowledge of an animal’s state while it is roaming the landscape.

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Quantifying mating behaviour using accelerometry and machine learning: challenges and opportunities

Aulsebrook,

Jacques-Hamilton,

Kempenaers

2024

Animal Behaviour

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Continuous on‐board behaviour classification using accelerometry: A case study with a new GPS‐3G‐Bluetooth system in Pacific black ducks

Cited by 13 publications

References 22 publications

Increasingly detailed insights in animal behaviours using continuous on-board processing of accelerometer data

Increasingly detailed insights in animal behaviours using continuous on-board processing of accelerometer data

Revealing increasingly detailed insights in animal behaviours using continuous on-board processing of accelerometer data

Quantifying mating behaviour using accelerometry and machine learning: challenges and opportunities

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