1. Species identification errors may have severe implications for the inference of species distributions. Accounting for misclassification in species distributions is an important topic of biodiversity research. With an increasing amount of biodiversity that comes from Citizen Science projects, where identification is not verified by preserved specimens, this issue is becoming more important. This has often been dealt with by accounting for false positives in species distribution models. However, the problem should account for misclassifications in general. 2. Here we present a flexible framework that accounts for misclassification in the distribution models and provides estimates of uncertainty around these estimates. The model was applied to data on viceroy, queen and monarch butterflies in the United States. The data were obtained from the iNaturalist database in the period 2019 to 2020. 3. Simulations and analysis of butterfly data showed that the proposed model was able to correct the reported abundance distribution for misclassification and also predict the true state for misclassified state.

翻译：1. 物种识别错误可能会对物种分布的推断产生严重影响。在生物多样性研究中，校正物种分布中的错误分类是一个重要课题。随着公民科学项目产生的生物多样性数据日益增多——这些数据中的物种鉴定未经保存标本验证——这一问题愈发凸显。以往研究通常通过在物种分布模型中考虑假阳性来处理该问题，但一般情况下的错误分类也需纳入考量。2. 本文提出一个灵活框架，可在分布模型中校正错误分类，并给出相关估算的不确定性估计。该模型应用于美国地区副王蛱蝶、君主斑蝶和黑脉金斑蝶的数据，数据来源为2019至2020年期间的iNaturalist数据库。3. 模拟实验与蝴蝶数据分析表明，所提模型能够校正所报告丰度分布中的错误分类，并预测被错误分类状态的真实情况。