Generalized site occupancy models allowing for false positive and false negative errors.
Generalized site occupancy models allowing for false positive and false negative errors.
复制标题
广义站点占用模型允许误报和漏报错误。
作者：
J. Andrew Royle;W. Link
已经开发了允许不完美的物种检测或“假阴性”观测的场地占用模型。这种模式已被广泛采用，在许多类群的调查。这些模型最基本的假设是，“假阳性”错误是不可能的。也就是说，一个物种如果没有出现，就无法被发现。然而，由于多种原因，在许多抽样情况下可能出现这种错误，如果不考虑这些错误，即使是很低的假阳性错误率也会导致对场地占用率的估计出现极端偏差。在本文中，我们开发了一个模型的网站占用率，允许假阴性和假阳性错误率。该模型可以表示为双组分有限混合物模型，并且可以使用免费提供的软件容易地拟合。我们提供了一个分析的鸟类调查数据，使用所提出的模型和目前的结果的一个简短的模拟研究评估的性能的最大似然估计和天真的估计在假阳性错误的存在。
Site occupancy models have been developed that allow for imperfect species detection or "false negative" observations. Such models have become widely adopted in surveys of many taxa. The most fundamental assumption underlying these models is that "false positive" errors are not possible. That is, one cannot detect a species where it does not occur. However, such errors are possible in many sampling situations for a number of reasons, and even low false positive error rates can induce extreme bias in estimates of site occupancy when they are not accounted for. In this paper, we develop a model for site occupancy that allows for both false negative and false positive error rates. This model can be represented as a two-component finite mixture model and can be easily fitted using freely available software. We provide an analysis of avian survey data using the proposed model and present results of a brief simulation study evaluating the performance of the maximum-likelihood estimator and the naive estimator in the presence of false positive errors.