Geo‐referenced species occurrences from public databases have become essential to biodiversity research and conservation. However, geographical biases are widely recognized as a factor limiting the usefulness of such data for understanding species diversity and distribution. In particular, differences in sampling intensity across a landscape due to differences in human accessibility are ubiquitous but may differ in strength among taxonomic groups and data sets. Although several factors have been described to influence human access (such as presence of roads, rivers, airports and cities), quantifying their specific and combined effects on recorded occurrence data remains challenging. Here we present sampbias, an algorithm and software for quantifying the effect of accessibility biases in species occurrence data sets. sampbias uses a Bayesian approach to estimate how sampling rates vary as a function of proximity to one or multiple bias factors. The results are comparable among bias factors and data sets. We demonstrate the use of sampbias on a data set of mammal occurrences from the island of Borneo, showing a high biasing effect of cities and a moderate effect of roads and airports. sampbias is implemented as a well‐documented, open‐access and user‐friendly R package that we hope will become a standard tool for anyone working with species occurrences in ecology, evolution, conservation and related fields.

中文翻译：

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sampbias，一种量化物种分布数据中地理采样偏差的方法

公共数据库中地理参考物种的出现已成为生物多样性研究和保护的关键。但是，地理偏见被广泛认为是限制此类数据对理解物种多样性和分布有用性的因素。特别是，由于人类可及性的差异，整个景观的采样强度差异是普遍存在的，但分类组和数据集之间的强度可能不同。尽管已经描述了影响人类进入的几个因素（例如道路，河流，机场和城市的存在），但是量化它们对记录的事件数据的特定影响和综合影响仍然具有挑战性。在这里，我们介绍了桑普比亚人，一种用于量化物种出现数据集中可访问性偏差的影响的算法和软件。sampbias使用贝叶斯方法来估计采样率如何随接近一个或多个偏差因子而变化。结果在偏倚因素和数据集之间是可比的。我们证明了在来自婆罗洲的哺乳动物发生的数据集上使用桑普比亚人，显示出城市的高度偏向效应以及道路和机场的中等偏向效应。sampbias是一个有据可查，开放获取且用户友好的R包，我们希望该包将成为从事生态，进化，保护和相关领域中物种出现的任何人的标准工具。