Generating pseudo-absence samples of invasive species based on outlier detection in the geographical characteristic space

Wentao Yang (), Huaxi He, Dongsheng Wei and Hao Chen ()
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Huaxi He: Hunan University of Science and Technology
Dongsheng Wei: Central South University of Forest and Technology
Hao Chen: Hunan University of Science and Technology

Journal of Geographical Systems, 2022, vol. 24, issue 2, No 7, 279 pages

Abstract: Abstract Obtaining the diversity samples of invasive alien species (species presence and absence samples) is vital for species distribution models. However, because of the enhanced focus on collecting presence samples, most datasets regarding invasive species lack explicit absence samples. Thus, the generation of effective pseudo-absence samples of invasive species is a critical issue for building species distribution models. This paper proposes a pseudo-absence sampling approach based on outlier detection in the geographical characteristic space. First, principal component analysis is used to model the linear correlation of the original variables, and a statistical index is built to determine the weight of the principal components. Next, in the geographical characteristic space built based on the principal components and their corresponding weights, the local outlier factor is obtained to identify the pseudo-absence samples. The dataset regarding the invasive species Erigeron annuus in the Yangtze River Economic Belt is used to illustrate the general process of the proposed approach. The prediction results from logistical regression with the proposed approach are better than these with the spatial random sampling, surface range envelope, and one-class support vector machine models. These findings validate the effectiveness of the proposed sampling approach.

Keywords: Invasive species; Spatial prediction; Spatial sampling; Principal component analysis; Local outlier detection (search for similar items in EconPapers)
JEL-codes: C13 C31 Q56 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10109-021-00362-6

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