The digitization of museum collections as well as an explosion in citizen science initiatives has resulted in a wealth of data that can be useful for understanding the global distribution of biodiversity, provided that the well-documented biases inherent in unstructured opportunistic data are accounted for. While traditionally used to model imperfect detection using structured data from systematic surveys of wildlife, occupancy-detection models provide a framework for modelling the imperfect collection process that results in digital specimen data. In this study, we explore methods for adapting occupancy-detection models for use with biased opportunistic occurrence data from museum specimens and citizen science platforms using 7 species of Anacardiaceae in Florida as a case study. We explored two methods of incorporating information about collection effort to inform our uncertainty around species presence: (1) filtering the data to exclude collectors unlikely to collect the focal species and (2) incorporating collection covariates (collection type and history of previous detections) into a model of collection probability. We found that the best models incorporated both the background data filtration step as well as the incorporation of collector covariates associated with the probability of collection. We found that month, method of collection and whether a collector had previously collected the focal species were important predictors of collection probability. Efforts to standardize meta-data associated with data collection will improve efforts for modeling the spatial distribution of a variety of species.

中文翻译：

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占用-收集模型：使用博物馆和草本植物的非结构化出现数据，对物种分布进行偏差校正建模

博物馆藏品的数字化以及公民科学计划的激增，导致了大量数据，这些数据可用于理解生物多样性的全球分布，但前提是要考虑到非结构化机会数据中固有的有据可查的偏见。传统上，使用来自野生动植物系统调查的结构化数据对不完善的检测进行建模时，占用检测模型提供了一个框架，用于对导致数字标本数据的不完善收集过程进行建模。在这项研究中，我们以佛罗里达州的7种漆树科为例，探索了利用博物馆样本和公民科学平台中的偏向机会发生数据来调整占用检测模型的方法。我们探索了两种结合收集工作信息的方法，以告知我们有关物种存在的不确定性：（1）过滤数据以排除不太可能收集重点物种的收集者；（2）将收集协变量（收集类型和先前检测的历史记录）纳入收集概率模型。我们发现最好的模型同时包含了背景数据过滤步骤以及与收集概率相关的收集器协变量的合并。我们发现该月份，收集方法以及收集者之前是否收集过焦点物种是收集概率的重要预测指标。标准化与数据收集相关的元数据的工作将改善对各种物种的空间分布进行建模的工作。