Defining appropriate null expectations for species distribution hypotheses is important because sampling bias and spatial autocorrelation can produce realistic, but ecologically meaningless, geographic patterns. Generating null species occurrences with similar spatial structure to observed data can help overcome these problems, but existing methods focus on single or pairs of species and do not incorporate between-species spatial structure that may occlude comparative biogeographic analyses. Here, we describe an algorithm for generating randomised species occurrence points that mimic the within- and between-species spatial structure of real datasets and implement it in a new R package – fauxcurrence. The algorithm can be implemented on any geographic domain for any number of species, limited only by computing power. To demonstrate its utility, we apply the algorithm to two common analysis-types: testing the fit of species distribution models (SDMs) and evaluating niche-overlap. The method works well on all tested datasets within reasonable timescales. We found that many SDMs, despite a good fit to the data, were not significantly better than null expectations and identified only two cases (out of a possible 32) of significantly higher niche divergence than expected by chance. The package is user-friendly, flexible and has many potential applications beyond those tested here, such as joint SDM evaluation and species co-occurrence analysis, spanning the areas of ecology, evolutionary biology and biogeography.

中文翻译：

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Fauxcurrence：在物种分布建模和生物地理学中模拟空模型的多物种发生

为物种分布假设定义适当的零期望很重要，因为抽样偏差和空间自相关可以产生现实但在生态上无意义的地理模式。生成具有与观测数据相似的空间结构的空物种出现可以帮助克服这些问题，但现有方法侧重于单个或成对的物种，并且不包含可能遮挡比较生物地理分析的物种间空间结构。在这里，我们描述了一种生成随机物种发生点的算法，该算法模拟真实数据集的物种内和物种间空间结构，并在新的 R 包中实现它——fauxcurrence. 该算法可以在任何地理域上针对任何数量的物种实施，仅受计算能力的限制。为了证明它的实用性，我们将该算法应用于两种常见的分析类型：测试物种分布模型 (SDM) 的拟合和评估生态位重叠。该方法在合理的时间范围内适用于所有测试数据集。我们发现许多 SDM 尽管与数据非常吻合，但并没有显着优于零预期，并且仅确定了两个案例（可能的 32 个案例中）的利基差异显着高于偶然预期。该软件包用户友好、灵活，除此处测试的应用外，还有许多潜在应用，例如联合 SDM 评估和物种共现分析，涵盖生态学、进化生物学和生物地理学领域。