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Spatial correlation structures for detections of individuals in spatial capture–recapture models
Biometrics ( IF 1.9 ) Pub Date : 2021-05-29 , DOI: 10.1111/biom.13502
Ben C Stevenson 1 , Rachel M Fewster 1 , Koustubh Sharma 2
Affiliation  

Spatial capture–recapture (SCR) models are commonly used to estimate animal density from surveys on which detectors passively detect animals without physical capture, for example, using camera traps, hair snares, or microphones. An individual is more likely to be recorded by detectors close to its activity center, the centroid of its movement throughout the survey. Existing models to account for this spatial heterogeneity in detection probabilities rely on an assumption of independence between detection records at different detectors conditional on the animals' activity centers, which are treated as latent variables. In this paper, we show that this conditional independence assumption may be violated due to the way animals move around the survey region and encounter detectors, such that additional spatial correlation is almost inevitable. We highlight the links between the well-studied issue of unmodeled temporal heterogeneity in nonspatial capture–recapture and this variety of unmodeled spatial heterogeneity in SCR, showing that the latter causes predictable bias in the same way as the former. We address this by introducing a latent detection field into the model, and illustrate the resulting approach with a simulation study and an application to a camera-trap survey of snow leopards Panthera uncia. Our method is a unifying model for several existing SCR approaches, with special cases including standard SCR, models that account for nonspatial individual heterogeneity, and models with overdispersed detection counts.

中文翻译:

空间捕获-再捕获模型中个体检测的空间相关结构

空间捕获-再捕获 (SCR) 模型通常用于通过调查来估计动物密度,在这些调查中,探测器被动地检测动物而不进行物理捕获,例如,使用相机陷阱、头发圈套或麦克风。一个人更有可能被靠近其活动中心的检测器记录下来,该活动中心是其在整个调查过程中移动的质心。用于解释检测概率中这种空间异质性的现有模型依赖于以动物活动中心为条件的不同检测器的检测记录之间的独立性假设,这些活动中心被视为潜在变量。在本文中,我们表明,由于动物在调查区域周围移动和遇到探测器的方式,这种条件独立性假设可能会被违反,这样额外的空间相关性几乎是不可避免的。我们强调了非空间捕获-再捕获中未建模的时间异质性这一经过充分研究的问题与 SCR 中这种未建模的空间异质性之间的联系,表明后者以与前者相同的方式导致可预测的偏差。我们通过在模型中引入潜在检测场来解决这个问题,并通过模拟研究和雪豹相机陷阱调查的应用来说明所产生的方法黑豹_ 我们的方法是几种现有 SCR 方法的统一模型,特殊情况包括标准 SCR、考虑非空间个体异质性的模型以及具有过度分散检测计数的模型。
更新日期:2021-05-29
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