1. Camera-trapping methods have been used to monitor movement and behavioural ecology parameters of wildlife. However, when considering movement behaviours to estimate DR is mandatory to include in the formulation the speed ratio, otherwise DR results will be biased. For instance, some wildlife populations present movement patterns characteristic of each behaviour (e.g. foraging or displacement between habitat patches), and further research is needed to integrate the behaviours in the estimation of movement parameters. In this respect, the day range (average daily distance travelled by an individual, DR) is a model parameter that relies on movement and behaviour. This study aims to provide a step forward concerning the use of camera-trapping in movement and behavioural ecology.
2. We describe a machine learning procedure to differentiate movement behaviours from camera-trap data, and revisit the approach to consider different behaviours in the estimation of DR. Second, working within a simulated framework we tested the performance of three approaches to estimate DR: DROB (i.e. estimating DR without behavioural identification), DRTB (i.e. estimating DR by identifying behaviours manually and weighting each behaviour on the basis of the encounter rate obtained) and DRRB (i.e. estimating DR based on the classification of movement behaviours by a machine learning procedure and the ratio between speeds). Finally, we evaluated these approaches for 24 wild mammal species with different behavioural and ecological traits.
3. The machine learning procedure to differentiate behaviours showed high accuracy (mean = 0.97). The DROB approach generated accurate results in scenarios with a speed-ratio (fast relative to slow behaviours) lower than 10, and for scenarios in which the animals spend most of the activity period on the slow behaviour. However, when considering movement behaviours to estimate DR is mandatory to include in the formulation the speed ratio, otherwise the DR results will be biased. The new approach, DRRB, generated accurate results in all the scenarios. The results obtained from real populations were consistent with the simulations.
4. In conclusion, the integration of behaviours and speed-ratio in camera-trap studies makes it possible to obtain unbiased DR. Speed-ratio should be considered so that fast behaviour is not overrepresented. The procedures described in this work extend the applicability of camera-trap-based approaches in both movement and behavioural ecology.

中文翻译：

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来自相机陷阱的运动和行为生态学创新：作为模型参数的白天范围

1. 相机捕捉方法已被用于监测野生动物的运动和行为生态参数。然而，当考虑运动行为来估计 DR 时，必须在公式中包含速度比，否则 DR 结果将有偏差。例如，一些野生动物种群呈现出每种行为特征的运动模式（例如觅食或栖息地斑块之间的位移），需要进一步研究将这些行为整合到运动参数的估计中。在这方面，天范围（个人每天行走的平均距离，DR）是依赖于运动和行为的模型参数。本研究旨在为在运动和行为生态学中使用相机诱捕迈出一步。
2. 我们描述了一个机器学习程序来区分运动行为与相机陷阱数据，并重新审视该方法以在 DR 估计中考虑不同的行为。其次，在模拟框架内，我们测试了三种估计 DR 方法的性能：DROB（即在没有行为识别的情况下估计 DR）、DRTB（即通过手动识别行为并根据获得的遭遇率对每个行为加权来估计 DR）和 DRRB（即根据机器学习程序对运动行为的分类和速度之间的比率来估计 DR）。最后，我们针对 24 种具有不同行为和生态特征的野生哺乳动物物种评估了这些方法。
3. 区分行为的机器学习程序显示出很高的准确性（平均值 = 0.97）。DROB 方法在速度比（快速相对于慢速行为）低于 10 的场景中以及动物将大部分活动时间用于慢速行为的场景中产生准确的结果。然而，当考虑运动行为来估计 DR 时，必须在公式中包含速度比，否则 DR 结果将有偏差。新方法 DRRB 在所有场景中都产生了准确的结果。从真实人群中获得的结果与模拟一致。
4. 总之，相机陷阱研究中行为和速度比的整合使得获得无偏的 DR 成为可能。应考虑速度比，以便快速行为不会过多。这项工作中描述的程序扩展了基于相机陷阱的方法在运动和行为生态学中的适用性。