occAssess : An R package for assessing potential biases in species occurrence data
occAssess : An R package for assessing potential biases in species occurrence data
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occAssess：用于评估物种发生数据中潜在偏差的 R 包
DOI：
10.1101/2021.04.19.440441
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Boyd R
中科院分区：
文献类型：
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作者：
Boyd R
来自各种来源的物种发生记录越来越多地汇总到异构数据库中，并提供给生态学家立即分析使用。然而，这些数据通常是有偏差的，即它们不是感兴趣的目标人群的概率样本，这意味着它们提供的信息可能不是现实的准确反映。因此，在将物种发生数据用于研究之前，对其进行适当审查至关重要。在这篇文章中，我们介绍occAssess，一个R软件包，可以直接筛选物种发生数据的潜在偏差。该软件包包含许多离散函数，每个函数返回分类、时间、空间和环境维度中一个或多个维度的潜在偏倚度量。用户可以选择提供一组时间段，将数据分成若干时间段;在这种情况下，将为每个时间段提供单独的输出，使该软件包特别有助于评估数据集是否适合估计物种分布的时间趋势。输出是可视化的（作为ggplot 2对象），并且不包括关于数据是否具有足够的质量以用于任何给定的推理用途的正式建议。相反，它们应该被用作辅助信息，并在被问到的问题的上下文中查看，以及正在使用的方法来回答it. We证明occAssess的实用性，通过将其应用于南美两个关键的传粉者类群的数据：叶鼻蝠（Phyllostomidae）和食蚜蝇（Syrphidae）。在这个示例中，我们简要评估了数据覆盖的各个方面随时间变化的程度。然后，我们讨论了包的其他应用程序，强调其局限性，并指出未来的发展机会。
Species occurrence records from a variety of sources are increasingly aggregated into heterogeneous databases and made available to ecologists for immediate analytical use. However, these data are typically biased, i.e. they are not a probability sample of the target population of interest, meaning that the information they provide may not be an accurate reflection of reality. It is therefore crucial that species occurrence data are properly scrutinised before they are used for research. In this article, we introduce occAssess, an R package that enables straightforward screening of species occurrence data for potential biases. The package contains a number of discrete functions, each of which returns a measure of the potential for bias in one or more of the taxonomic, temporal, spatial, and environmental dimensions. Users can opt to provide a set of time periods into which the data will be split; in this case separate outputs will be provided for each period, making the package particularly useful for assessing the suitability of a dataset for estimating temporal trends in species' distributions. The outputs are provided visually (as ggplot2 objects) and do not include a formal recommendation as to whether data are of sufficient quality for any given inferential use. Instead, they should be used as ancillary information and viewed in the context of the question that is being asked, and the methods that are being used to answer it. We demonstrate the utility of occAssess by applying it to data on two key pollinator taxa in South America: leaf‐nosed bats (Phyllostomidae) and hoverflies (Syrphidae). In this worked example, we briefly assess the degree to which various aspects of data coverage appear to have changed over time. We then discuss additional applications of the package, highlight its limitations, and point to future development opportunities.