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利用加速度计数据的连续机载处理,对动物行为有了越来越详细的见解。

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

作者信息

Yu Hui, Klaassen Chris A J, Deng Jian, Leen Trent, Li Guozheng, Klaassen Marcel

机构信息

Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Geelong, VIC, Australia.

Druid Technology Co., Ltd, Chengdu, Sichuan, China.

出版信息

Mov Ecol. 2022 Oct 24;10(1):42. doi: 10.1186/s40462-022-00341-6.

DOI:10.1186/s40462-022-00341-6
PMID:36280879
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9594961/
Abstract

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.

摘要

背景

动物行为、生态和生理学研究不断受益于生物记录技术的进步,包括收集加速度计数据以推断动物行为和能量消耗。在该领域的最新技术进展之一中,将原始加速度计数据进行机载处理以识别动物行为,被证明具有高度的能源、重量和成本效益,从而能够在广泛的动物追踪研究中,除了常规位置数据外,还能持续收集行为数据。

方法

我们应用了这一最新进展,从6只太平洋黑鸭(Anas superciliosa)收集连续行为记录,以评估这项新技术相对于缺乏行为数据或仅间歇性记录加速度计数据的追踪研究的一些潜在优势。我们(i)比较了连续记录与不同时间间隔采样的行为之间的时间活动预算差异,(ii)比较了使用每小时GPS定位且有和没有额外行为数据时的每日飞行总距离,以及(iii)探讨了行为记录如何为动物活动范围研究提供额外见解。

结果

利用6只个体鸭总共690天的行为记录,区分出8种不同行为,我们说明了如果使用连续而非间隔采样的加速度计数据,时间活动预算准确性会得到怎样的提高。值得注意的是,对于飞行和奔跑等罕见行为,当采样间隔超过10分钟时,误差率>1很常见。在一只个体鸭中,结合72天的每小时GPS定位和同期的连续行为记录,我们表明基于行为的每日距离估计比根据每小时采样的GPS定位计算出的距离显著更高(高达540%)。同样,利用同一只个体鸭的72天数据,我们展示了该个体如何在其整个活动范围内利用特定地点来满足特定需求(例如栖息和觅食)。

结论

我们表明,通过使用能够连续记录动物行为的追踪器,可以在时间活动预算和每日行进距离的估计方面取得实质性改进。通过将行为纳入活动范围估计,我们还强调,这种新颖的追踪技术不仅可能改善估计,还可能为动物行为研究开辟新途径,重要的是提高我们对动物在野外活动时状态的了解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca2d/9594961/e7230b5ed865/40462_2022_341_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca2d/9594961/497aefa1af22/40462_2022_341_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca2d/9594961/ec12483aff2c/40462_2022_341_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca2d/9594961/6bfa56a0ea7d/40462_2022_341_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca2d/9594961/a2254657e3cd/40462_2022_341_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca2d/9594961/b12fd0465e6f/40462_2022_341_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca2d/9594961/e7230b5ed865/40462_2022_341_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca2d/9594961/497aefa1af22/40462_2022_341_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca2d/9594961/ec12483aff2c/40462_2022_341_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca2d/9594961/6bfa56a0ea7d/40462_2022_341_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca2d/9594961/a2254657e3cd/40462_2022_341_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca2d/9594961/b12fd0465e6f/40462_2022_341_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca2d/9594961/e7230b5ed865/40462_2022_341_Fig6_HTML.jpg

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