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.

获取外来入侵物种的多样性样本（物种存在和不存在样本）对于物种分布模型至关重要。然而，由于更加注重收集存在样本，大多数关于入侵物种的数据集缺乏明确的缺失样本。因此，生成入侵物种的有效伪缺失样本是构建物种分布模型的关键问题。本文提出了一种基于地理特征空间异常值检测的伪缺失采样方法。首先，通过主成分分析对原始变量的线性相关性进行建模，并建立统计指标来确定主成分的权重。接下来，在基于主成分及其对应权重构建的地理特征空间中，获得局部异常值因子以识别伪缺失样本。入侵物种数据集以长江经济带的Erigeron annuus为例说明了该方法的一般过程。使用所提出的方法进行逻辑回归的预测结果优于使用空间随机采样、表面范围包络和一类支持向量机模型的预测结果。这些发现验证了所提议的抽样方法的有效性。