Use of tri-axial accelerometers to assess terrestrial mammal behaviour in the wild
Use of tri-axial accelerometers to assess terrestrial mammal behaviour in the wild
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DOI：
10.1111/jzo.12308
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发表时间：
2016-04-01
影响因子：
2
通讯作者：
Wheeler, P.
中科院分区：
文献类型：
--
作者：
Lush, L.;Ellwood, S.;Wheeler, P.
三轴加速度计标签提供有关身体运动的定量数据，可用于以其他方式不可能实现的行为特征和了解物种生态。在自然环境中，对野生陆地哺乳动物（尤其是较小的物种）使用标签受到限制。电池电量不足也减少了收集的数据量，这限制了对动物行为的推断。使用野生动物的另一个挑战是获取对实际行为的观察，以比较标签数据并创建足够的训练集以可靠地识别行为状态。给棕色野兔安装加速度计 5 周，以评估其在收集详细行为数据和活动水平方面的用途。对戴着项圈的野兔进行拍摄，将其实际行为与标签数据联系起来。使用随机森林（集成学习方法）对观察到的行为进行分类，以创建监督模型，然后用于根据标签对野兔行为进行分类。标签寿命的延长允许从每个个体获取大量数据并直接观察标记野兔的行为。随机森林以 11% 的错误率对标签数据中观察到的行为进行了准确分类。个体行为的准确度因跑步（100% 准确度）、进食（94.7%）和警惕（98.3%）而变化，分类准确度最高。野兔在活动时将 46% 的时间用于保持警惕，25% 的时间用于进食。我们的标签和随机森林的结合促进了在观察研究可能有限或不可能的情况下收集大量动物行为数据。同样的方法可以用于一系列陆生哺乳动物，以创建模型来研究标签数据的行为，以更多地了解它们的行为并用于回答许多生态问题。然而，需要进一步开发分析标签数据的方法，以使该过程更快、更简单、更准确。
Tri-axial accelerometer tags provide quantitative data on body movement that can be used to characterize behaviour and understand species ecology in ways that would otherwise be impossible. Using tags on wild terrestrial mammals, especially smaller species, in natural settings has been limited. Poor battery power also reduced the amount of data collected, which limits what can be derived about animal behaviour. Another challenge using wild animals, is acquiring observations of actual behaviours with which to compare tag data and create an adequate training set to reliably identify behavioural states. Brown hares were fitted with accelerometers for 5weeks to evaluate their use in collecting detailed behaviour data and activity levels. Collared hares were filmed to associate actual behaviours with tag data. Observed behaviours were classified using Random Forests (ensemble learning method) to create a supervised model and then used to classify hare behaviour from the tags. Increased tag longevity allowed acquisition of large quantities of data from each individual and direct observation of tagged hare's behaviour. Random Forests accurately classified observed behaviours from tag data with an 11% error rate. Individual accuracy of behaviours varied with running (100% accuracy), feeding (94.7%) and vigilance (98.3%) having the highest classification accuracy. Hares spent 46% of their time being vigilant and 25% feeding when active. The combination of our tags and Random Forests facilitated large amounts of behavioural data to be collected on animals where observational studies could be limited, or impossible. The same method could be used on a range of terrestrial mammals to create models to investigate behaviour from tag data, to learn more about their behaviour and be used to answer many ecological questions. However, further development of methods for analysing tag data is needed to make the process quicker, simpler and more accurate.