Abundance estimation is a vital goal in wildlife monitoring. Camera-traps are a tool to survey wildlife populations noninvasively and can be used for abundance estimation if individuals are identifiable. However, for species without individual identification characteristics, camera-trap surveys have often been combined with some other survey method such as capture-recapture (CR, using traditional tags or DNA through hair snags or scat) to inform an integrated model. We discuss and apply two integrated models involving presence-absence data from camera traps and CR data from hair traps to compare bias and precision to estimate the population density of grizzly bears of the central Rocky Mountains of Alberta, Canada. Unlike many other studies, we found that integrating presence-absence data with CR data does not improve the precision of the density estimates. The possible reasons for such results are discussed in detail.

中文翻译：

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拍摄丰富：比较相机陷阱和头发陷阱数据的集成多采样模型

丰度估计是野生动物监测的重要目标。照相机陷阱是一种非侵入性地调查野生动物种群的工具，如果个体是可识别的，则可用于丰度估计。然而，对于没有个体识别特征的物种，相机陷阱调查通常与其他一些调查方法相结合，例如捕获 - 再捕获（CR，使用传统标签或通过头发障碍或粪便的 DNA）来告知综合模型。我们讨论并应用了两个集成模型，涉及来自相机陷阱的存在-不存在数据和来自头发陷阱的 CR 数据，以比较偏差和精度来估计加拿大艾伯塔省落基山脉中部灰熊的种群密度。与许多其他研究不同，我们发现将存在-不存在数据与 CR 数据相结合并不能提高密度估计的精度。详细讨论了此类结果的可能原因。