Biological records are often the data of choice for training predictive species distribution models (SDMs), but spatial sampling bias is pervasive in biological records data at multiple spatial scales and is thought to impair the performance of SDMs. We simulated presences and absences of virtual species as well as the process of recording these species to evaluate the effect on species distribution model prediction performance of (1) spatial bias in training data, (2) sample size (the average number of observations per species), and (3) the choice of species distribution modelling method. Our approach is novel in quantifying and applying real-world spatial sampling biases to simulated data. Spatial bias in training data decreased species distribution model prediction performance, but sample size and the choice of modelling method were more important than spatial bias in determining the prediction performance of species distribution models.

中文翻译：

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对于预测物种分布建模，数据量比其空间偏差更重要

生物记录通常是训练预测物种分布模型 (SDM) 的首选数据，但空间采样偏差在多个空间尺度的生物记录数据中普遍存在，并且被认为会损害 SDM 的性能。我们模拟了虚拟物种的存在和缺失以及记录这些物种的过程，以评估 (1) 训练数据中的空间偏差，(2) 样本大小（每个物种的平均观察次数）对物种分布模型预测性能的影响)，以及 (3) 物种分布建模方法的选择。我们的方法在量化现实世界空间采样偏差并将其应用于模拟数据方面是新颖的。训练数据中的空间偏差降低了物种分布模型的预测性能，