Premium
Outlier detection methods are still effective even using virtual species created with the probabilistic approach
Author(s) -
Liu Canran,
White Matt,
Newell Graeme
Publication year - 2020
Publication title -
journal of biogeography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.7
H-Index - 158
eISSN - 1365-2699
pISSN - 0305-0270
DOI - 10.1111/jbi.13872
Subject(s) - outlier , probabilistic logic , anomaly detection , biogeography , computer science , statistical model , data mining , artificial intelligence , biology , ecology
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.