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Mark-Recapture and Mark-Resight Methods for Estimating Abundance with Remote Cameras: A Carnivore Case Study

机译:通过远程摄像机估算丰度的标记重获和标记重视方法:肉食动物案例研究

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摘要

Abundance estimation of carnivore populations is difficult and has prompted the use of non-invasive detection methods, such as remotely-triggered cameras, to collect data. To analyze photo data, studies focusing on carnivores with unique pelage patterns have utilized a mark-recapture framework and studies of carnivores without unique pelage patterns have used a mark-resight framework. We compared mark-resight and mark-recapture estimation methods to estimate bobcat (Lynx rufus) population sizes, which motivated the development of a new "hybrid" mark-resight model as an alternative to traditional methods. We deployed a sampling grid of 30 cameras throughout the urban southern California study area. Additionally, we physically captured and marked a subset of the bobcat population with GPS telemetry collars. Since we could identify individual bobcats with photos of unique pelage patterns and a subset of the population was physically marked, we were able to use traditional mark-recapture and mark-resight methods, as well as the new “hybrid” mark-resight model we developed to estimate bobcat abundance. We recorded 109 bobcat photos during 4,669 camera nights and physically marked 27 bobcats with GPS telemetry collars. Abundance estimates produced by the traditional mark-recapture, traditional mark-resight, and “hybrid” mark-resight methods were similar, however precision differed depending on the models used. Traditional mark-recapture and mark-resight estimates were relatively imprecise with percent confidence interval lengths exceeding 100% of point estimates. Hybrid mark-resight models produced better precision with percent confidence intervals not exceeding 57%. The increased precision of the hybrid mark-resight method stems from utilizing the complete encounter histories of physically marked individuals (including those never detected by a camera trap) and the encounter histories of naturally marked individuals detected at camera traps. This new estimator may be particularly useful for estimating abundance of uniquely identifiable species that are difficult to sample using camera traps alone.
机译:食肉动物种群的丰度估计很困难,并已促使使用非侵入式检测方法(例如,远程触发的摄像头)来收集数据。为了分析照片数据,针对具有独特破损图案的食肉动物的研究采用了标记捕获框架,而针对没有独特破损图案的食肉动物的研究采用了标记审查框架。我们比较了标记监督和标记获取估计方法来估计山猫(Lynx rufus)的种群大小,这激发了新的“混合”标记监督模型的发展,以替代传统方法。我们在整个加利福尼亚南部市区的研究区域部署了一个由30个摄像机组成的采样网格。此外,我们用GPS遥测项圈捕获并标记了山猫种群的一部分。由于我们可以用独特的羊皮图案照片识别出山猫,并且对一部分人口进行了物理标记,因此我们能够使用传统的标记捕获和标记识别方法,以及新的“混合”标记识别模型,开发来估计山猫的丰度。我们在4,669个相机之夜记录了109张山猫照片,并用GPS遥测项圈对27条山猫进行了物理标记。传统标记回收,传统标记回收和“混合”标记回收方法得出的丰度估计值相似,但是精度因所使用的模型而异。传统的标记回收和标记修订估计相对不准确,置信区间长度百分比超过点估计的100%。混合标记检验模型产生的精度更高,置信区间百分比不超过57%。混合标记识别方法的精度提高源于利用物理标记个体(包括从未通过相机陷阱检测到的个体)的完整遭遇历史以及在相机陷阱中检测到的自然标记个体的遭遇历史。这种新的估算器对于估算难以单独使用相机陷阱进行采样的独特可识别物种的丰富度尤其有用。

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