How to make use of unlabeled observations in species distribution modeling using point process models. Issue 10 (1st April 2021)

• Record Type: Journal Article
• Authors: Guilbault, Emy; Renner, Ian; Mahony, Michael; Beh, Eric
• Is Part Of: Ecology and evolution. Volume 11:Issue 10(2021)
• Page Start: 5220
• Page End: 5243
• Publication Date: 2021-04-01
• Subjects: classification -- ecological statistics -- EM algorithm -- machine learning -- misidentification -- mixture modeling -- presence‐only data; Ecology -- Periodicals; Evolution -- Periodicals; 577.05
• DOI: 10.1002/ece3.7411
• Languages: English
• ISSNs: 2045-7758

Abstract

Abstract: 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. 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. 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 did not use the misspecified data. We applied the best‐performing methods to a dataset of three frog species ( Mixophyes ). 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. Abstract : Data quality in species distribution modeling is a central interest to improve species distribution prediction. We developed two algorithms with 7 initialization methods to classify data with uncertain species identity. We use simulations to test the model parameters and choose the best methods to apply to Myxophies genus species.