Detection error can bias observations of ecological processes, especially when some species are never detected during sampling. In many communities, the probable identity of these missing species is known from previous research and natural history collections, but this information is rarely incorporated into subsequent models. Here, I present prior aggregation as a method for including information from external sources in Bayesian hierarchical detection models. Prior aggregation combines information from multiple prior distributions, in this case, an ecologically informative, species-level prior, and an uninformative community-level prior. This approach incorporates external information into the model without sacrificing the advantages of modeling species in the context of the community. Using simulated data supplied to a multispecies occupancy model, I demonstrated that prior aggregation improves estimates of (1) metacommunity richness and (2) environmental covariates were associated with species-specific occupancy probabilities. When applied to a dataset of small mammals in Vermont, prior aggregation allowed the model to estimate occupancy correlates of the Eastern cottontail Sylvilagus floridanus, a species observed at several sites in the region but never captured. Prior aggregation can be used to improve the analysis of several important metrics in population and community ecology, including abundance, survivorship, and diversity.

中文翻译：

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生态学先验改进了贝叶斯模型对物种丰富度和未发现物种占有率的估计

检测误差可能会使生态过程的观察产生偏差，特别是当某些物种在采样过程中从未检测到时。在许多群落中，这些失踪物种的可能身份是从以前的研究和自然历史收藏中得知的，但这些信息很少被纳入后续模型中。在这里，我提出先验聚合作为将来自外部源的信息包含在贝叶斯分层检测模型中的方法。先验聚合结合了来自多个先验分布的信息，在这种情况下，是生态信息丰富的物种级先验和无信息的社区级先验。这种方法将外部信息纳入模型中，而不会牺牲在群落背景下建模物种的优势。使用提供给多物种占用模型的模拟数据，我证明了先前的聚合提高了对（1）元群落丰富度和（2）环境协变量与物种特定占用概率相关的估计。当应用于佛蒙特州小型哺乳动物的数据集时，先前的聚合使模型能够估计东部棉尾Sylvilagus floridanus的占用相关性，该物种在该地区的多个地点观察到但从未捕获。预先聚合可用于改进对人口和群落生态学中几个重要指标的分析，包括丰度、存活率和多样性。