Myxophies species location and environmental variables

1. Species distribution modeling, 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 such models. However, the species observations used can have
varying levels of quality and can have incomplete information, such as
uncertain or unknown species identity. 2. In this paper, we develop two
algorithms to classify observations with unknown species identities which
simultaneously predict several species distributions using spatial point
processes. Through simulations, we compare the performance of these
algorithms using 7 different initializations to the performance of models
fitted using only the observations with known species identity. 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.
We applied the best-performing methods to a dataset of three frog species
(Mixophyes). 4. These models represent a helpful and promising tool for
opportunistic surveys where misidentification is possible or for the
distribution of species newly separated in their taxonomy.