1. Species distribution modelling, which allows users to predict the
spatial distribution of species with the use of environmental
covariates, has become increasingly popular, with many software
platforms providing tools to fit species distribution models. However,
the species observations used in species distribution models can have
varying levels of quality and can have incomplete information, such as
uncertain species identity. 2. In this paper, we develop two algorithms
to reclassify observations with unknown species identities which
simultaneously predict different species distributions using spatial
point processes. We compare the performance of the different algorithms
using different initializations and parameters with models fitted using
only the observations with known species identity through simulations.
3. We show that performance varies with differences in correlation among
species distributions, species abundance, and the proportion of
observations with unknown species identities. Additionally, some of the
methods developed here outperformed the models that didn't use the
misspecified data. 4. These models represent an helpful and promising
tool for opportunistic surveys where misidentification happens or for
the distribution of species newly separated in their taxonomy.