Outlier detection methods are still effective even using virtual species created with the probabilistic approach. (15th May 2020)

• Record Type: Journal Article
• Authors: Liu, Canran; White, Matt; Newell, Graeme
• Is Part Of: Journal of biogeography. Volume 47:Number 9(2020:Sep.)
• Page Start: 2054
• Page End: 2057
• Publication Date: 2020-05-15
• Subjects: observation error -- outlier detection -- probabilistic approach -- random forest -- species distribution data -- species distribution model -- threshold approach -- virtual species; Biogeography -- Periodicals; 578.09
• DOI: 10.1111/jbi.13872
• Languages: English
• ISSNs: 0305-0270

Abstract

Abstract: Liu et al. ( Journal of Biogeography, 2018, 45 :164–176) presented an approach to detect outliers in species distribution data by developing virtual species created using the threshold approach. Meynard et al. ( Journal of biogeography, 2019, 46 :2141–2144) raised concerns about this approach stating that 'using a probabilistic approach … may significantly change results'. Here we provide a new series of simulations using the two approaches and demonstrate that the outlier detection approach based on pseudo species distribution models was still effective when using the probabilistic approach, although the detection rate was lower than when using the threshold approach.