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. 动物密度估算的高效可靠方法对野生动物保护与管理至关重要。相机陷阱（camera trapping）是当前野生动物研究领域日益流行的研究手段，随着技术迭代——例如支持动物行为观测的视频录制功能——其应用潜力进一步得到挖掘。即便无法实现个体识别，这类行为观测信息也可用于动物密度估算。尽管目前已开发出多种适用于无个体标记物种（即无标识种群）的模型，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2008年提出的随机相遇模型（random encounter model, REM）的扩展。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系，可通过频率学派（frequentist）或贝叶斯（Bayesian）方法进行参数估计。本研究采用蒙特卡洛（Monte Carlo）模拟对REST模型的可靠性与可行性进行了检验；同时将该方法应用于非洲热带雨林场景，并与样线调查（line-transect survey）结果进行对比。
3. 模拟结果表明，REST模型可实现动物密度的无偏估计。即便个体间移动速度存在差异，或动物以成对方式移动时，该模型仍能给出无偏的密度估计结果。但REST模型对个体间活动节律不同步的情况较为敏感。此外，需使用搭载快速且可靠的红外传感器的相机设备，并合理设置相机陷阱的参数（如视频录制时长、触发延迟时间）。野外调查结果显示，非洲热带雨林中两种有蹄类（ungulate）动物的停留时间与时间参数分布拟合度良好，且REST模型得到的密度估计结果与样线调查结果一致。
4. 总结与应用。在无需个体识别的动物密度估算场景中，随机相遇与停留时间（REST）模型的效率与可行性均优于传统随机相遇模型（REM）。若能合理应用REST模型，有望实现多种无个体识别标记的陆生脊椎动物的密度估算，因此该方法可作为种群监测的有效手段。