Myxophies species location and environmental variables
Cite this dataset
Guilbault, Emy; Renner, Ian; Mahony, Michael; Beh, Eric (2022). Myxophies species location and environmental variables [Dataset]. Dryad. https://doi.org/10.5061/dryad.vx0k6djqw
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.
Our study case dataset uses presence-only records from the online database of the Atlas of Living Australia. On this platform, any person that sees a frog in the wild can report the coordinates and other relevant information. We focused the analysis on the three northern species of Mixophyes genus that have been recently separated in Mahony et al. 2006. We cleaned our dataset by including only observations of adult specimens with date information and through verification by a specialist of these species, M. Mahony. The observations with known species labels were those for which we have associated genetic information as well as any observations reported after the taxonomic split in 2006. The rest of the observations were considered as having unknown species labels.
We extracted relevant covariates for these species on a 5kmx5km grid from different sources: BBCVL, UC Davis Biogeo group and bom.
A README file details the data files and an Rscript documents details of the steps of the analysis.