Data from: Estimating animal density without individual recognition using information derivable exclusively from camera traps

1. Efficient and reliable methods for estimating animal density are essential to wildlife conservation and management. Camera trapping is an increasingly popular tool in this area of wildlife research , with further potential arising from technological improvements, such as video recording functions that allow for behavioural observation of animals. This information may be useful in the estimation of animal density, even without individual recognition. Although several models applicable to species lacking individual markings (i.e. unmarked populations) have been developed, a methodology incorporating behavioural information from videos has not yet been established.

2. We developed a likelihood-based model: the random encounter and staying time (REST) model. It is an extension of the random encounter model (REM) by Rowcliffe et al. (2008). The REST model describes the relationship among staying time, trapping rate, and density, which is estimable using a frequentist or Bayesian approach. We tested the reliability and feasibility of the REST model using Monte Carlo simulations. We also applied the approach in the African rainforest and compared the results with those of a line-transect survey.

3. The simulations showed that the REST model provided unbiased estimates of animal density. Even when animal movement speeds varied among individuals, and when animals travelled in pairs, the model provided unbiased density estimates. However, the REST model was vulnerable to unsynchronized activity patterns among individuals. Moreover, it is necessary to use a camera model with a fast and reliable infrared sensor, and to set the camera trap’s parameters appropriately (i.e. video length, delay period). The field survey showed that the staying time of two ungulate species in the African rainforest exhibited good fit with a temporal parametric distribution, and the REST model provided density estimates consistent with those of a line-transect survey.

4. Synthesis and applications. The random encounter and staying time (REST) model provides better efficiency and higher feasibility than the random encounter model in estimating animal density without individual recognition. Careful application of the REST model should provide the potential to estimate density of many ground-dwelling vertebrates lacking individually recognizable markings, and thus should be an effective method for population monitoring.

1. 精准高效的动物密度估算方法，是野生动物保护与管理工作的核心支撑。相机陷阱监测（Camera trapping）是野生动物研究领域愈发常用的技术手段，其应用潜力随技术迭代持续拓展，例如支持动物行为观测的视频录制功能。即便无法识别个体，这类行为观测数据也可用于动物密度估算。尽管学界已开发出多款适用于无个体识别标记物种（即无标记种群）的密度估算模型，但整合视频行为信息的估算方法仍未建立。
2. 本研究开发了一种基于似然的模型：随机相遇与停留时间（random encounter and staying time, REST）模型。该模型是Rowcliffe等人于2008年提出的随机相遇模型（random encounter model, REM）的拓展版本。REST模型刻画了停留时间、相机捕获率与种群密度三者间的关联关系，可通过频率学派或贝叶斯学派方法进行参数估算。本研究通过蒙特卡洛模拟（Monte Carlo simulations）验证了REST模型的可靠性与可行性；同时将该方法应用于非洲热带雨林区域，并与样线调查（line-transect survey）结果进行对比。
3. 模拟结果表明，REST模型可给出无偏的动物密度估算值。即便不同个体的移动速度存在差异，或是动物以成对形式移动，该模型仍能生成无偏的密度估算结果。但该模型对个体间活动节律不同步的情况较为敏感。此外，需选用搭载快速且稳定红外传感器的相机设备，并合理配置相机陷阱的参数（如视频时长、触发延迟时长）。野外调查结果显示，非洲热带雨林中两种有蹄类动物的停留时间符合时间参数分布特征，且REST模型得到的密度估算结果与样线调查结果一致。
4. 综合与应用。相较于随机相遇模型，REST模型在无个体识别标记的动物密度估算中具备更高的效率与可行性。若能规范应用REST模型，便可实现众多无个体识别标记的陆生脊椎动物的密度估算，因此该模型可作为种群监测的有效手段。