  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.
