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

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Listed:
  • Wentao Yang

    (Hunan University of Science and Technology)

  • 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)

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.

Suggested Citation

  • Wentao Yang & Huaxi He & Dongsheng Wei & Hao Chen, 2022. "Generating pseudo-absence samples of invasive species based on outlier detection in the geographical characteristic space," Journal of Geographical Systems, Springer, vol. 24(2), pages 261-279, April.
  • Handle: RePEc:kap:jgeosy:v:24:y:2022:i:2:d:10.1007_s10109-021-00362-6
    DOI: 10.1007/s10109-021-00362-6
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    References listed on IDEAS

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    More about this item

    Keywords

    Invasive species; Spatial prediction; Spatial sampling; Principal component analysis; Local outlier detection;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth

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