How to make use of unlabeled observations in species distribution modeling using point process models

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Guilbault , E P , Renner , I , Mahony , M & Beh , E 2021 , ' How to make use of unlabeled observations in species distribution modeling using point process models ' , Ecology and Evolution , vol. 11 , no. 10 , pp. 5220-5243 . https://doi.org/10.1002/ece3.7411

Title: How to make use of unlabeled observations in species distribution modeling using point process models
Author: Guilbault, Emy Paulette; Renner, Ian; Mahony, Michael; Beh, Eric
Contributor: University of Helsinki, Organismal and Evolutionary Biology Research Programme
Date: 2021-05
Language: eng
Number of pages: 24
Belongs to series: Ecology and Evolution
ISSN: 2045-7758
URI: http://hdl.handle.net/10138/335681
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.
Subject: 1181 Ecology, evolutionary biology
classification
ecological statistics
EM algorithm
machine learning
misidentification
mixture modeling
presence&#8208
only data
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