The random encounter model, a method for estimating animal density using camera traps without the need for individual recognition, has been developed over the past decade. A key assumption of this model is that cameras are placed randomly in relation to animal movements, requiring that cameras are not set only at sites thought to have high animal traffic. The aim of this study was to define a correction factor that allows the random encounter model to be applied in photo-trapping surveys in which cameras are placed along tracks to maximize capture probability. Our hypothesis was that applying such a correction factor would compensate for the different rates at which lynxes use tracks and the surrounding area, and should thus improve the estimates obtained with the random encounter model. We tested this using data from a well-known Iberian lynx Lynx pardinus population. Firstly, we estimated Iberian lynx densities using a traditional camera-trapping design followed by spatially explicit capture–recapture analyses. We estimated the differential use rate for tracks vs the surrounding area using data from a lynx equipped with a GPS collar, and subsequently calculated the correction factor. As expected, the random encounter model overestimated densities by 378%. However, the application of the correction factor improved the estimate and reduced the error to 16%. Although there are limitations to the application of the correction factor, the corrected random encounter model shows potential for density estimation of species for which individual identification is not possible.

中文翻译：

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使用传统相机陷阱设计生成的数据改进随机遭遇模型方法以估计食肉动物密度

随机遭遇模型是一种使用相机陷阱估计动物密度而不需要个体识别的方法，在过去十年中得到了发展。该模型的一个关键假设是摄像头是随机放置的，与动物的运动有关，这要求摄像头不仅仅设置在被认为具有高动物流量的地点。本研究的目的是定义一个校正因子，允许将随机遭遇模型应用于照片捕获调查，其中相机沿着轨道放置以最大化捕获概率。我们的假设是，应用这样的校正因子将补偿猞猁使用轨道和周围区域的不同速率，因此应该改进随机遭遇模型获得的估计。我们使用来自著名的伊比利亚猞猁的数据对此进行了测试猞猁人口。首先，我们使用传统的相机捕获设计，然后进行空间显式捕获-再捕获分析来估计伊比利亚猞猁的密度。我们使用来自配备 GPS 项圈的山猫的数据估计了轨道与周围区域的差异使用率，并随后计算了校正因子。正如预期的那样，随机遭遇模型高估了 378% 的密度。然而，校正因子的应用改进了估计并将误差降低到 16%。尽管校正因子的应用存在局限性，但校正后的随机遭遇模型显示了对无法进行个体识别的物种进行密度估计的潜力。