Environmental DNA is a survey tool with rapidly expanding applications for assessing the presence of a species at surveyed sites. Environmental DNA methodology is known to be prone to false negative and false positive errors at the data collection and laboratory analysis stages. Existing models for environmental DNA data require augmentation with additional sources of information to overcome identifiability issues of the likelihood function and do not account for environmental covariates that predict the probability of species presence or the probabilities of error. We present a novel Bayesian model for analysing environmental DNA data by proposing informative prior distributions for logistic regression coefficients that enable us to overcome parameter identifiability, while performing efficient Bayesian variable selection. Our methodology does not require the use of transdimensional algorithms and provides a general framework for performing Bayesian variable selection under informative prior distributions in logistic regression models.

中文翻译：

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模拟环境DNA数据；贝叶斯变量选择考虑了假阳性和假阴性错误

环境DNA是一种调查工具，其应用范围正在迅速扩大，用于评估被调查地点物种的存在。众所周知，环境DNA方法在数据收集和实验室分析阶段容易出现假阴性和假阳性错误。现有的环境DNA数据模型需要使用其他信息源进行扩充，以克服似然函数的可识别性问题，并且无法考虑预测物种存在或错误概率的环境协变量。我们提出了一种新颖的贝叶斯模型，用于分析环境DNA数据，方法是为logistic回归系数提供信息性先验分布，从而使我们能够克服参数可识别性，同时执行有效的贝叶斯变量选择。