Fauxcurrence: simulating multi-species occurrences for null models in species distribution modelling and biogeography
Fauxcurrence: simulating multi-species occurrences for null models in species distribution modelling and biogeography
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虚假发生：在物种分布建模和生物地理学中模拟零模型的多物种发生
DOI：
10.1111/ecog.05880
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发表时间：
2022
期刊：
影响因子：
5.9
通讯作者：
Osborne O
中科院分区：
文献类型：
--
作者：
Osborne O
为物种分布假设定义适当的零期望是很重要的，因为抽样偏差和空间自相关可以产生现实的，但生态上毫无意义的地理格局。生成具有与观测数据相似的空间结构的空种出现可以帮助克服这些问题，但是现有方法专注于单个或成对的物种，并且不包括可能阻碍比较地理分析的物种间空间结构。在这里，我们描述了一种算法，用于生成随机物种发生点，模仿内和物种之间的空间结构的真实的数据集，并实现它在一个新的R包-fauxcurrence。该算法可以在任何地理域上实现任何数量的物种，仅受计算能力的限制。为了证明其实用性，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尺度内对所有测试数据集都有效。我们发现，许多SDM，尽管一个很好的拟合数据，并没有显着优于零预期，并确定只有两个情况下（可能的32）的利基分歧显着高于预期的机会。该软件包是用户友好的，灵活的，有许多潜在的应用超出了这里测试的，如联合SDM评估和物种共现分析，跨越生态学，进化生物学和地理学领域。
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.
解开物种分布模型中空间自相关的驱动因素
DOI：
--
发表时间：
2020
期刊：
影响因子：
--
作者：
Konrad P. Mielke;Tom Claassen;Michela Busana;T. Heskes;M. Huijbregts;K. Koffijberg;A. Schipper
通讯作者：
A. Schipper
影响因子：
6.4
作者：
Algar, Adam C.;Mahler, D. Luke;Losos, Jonathan B.
通讯作者：
Losos, Jonathan B.
空间自相关扩大了生态位宽度-范围大小关系
影响因子：
6.4
作者：
T. Moore;Robert Bagchi;Matthew E. Aiello‐Lammens;C. Schlichting
通讯作者：
C. Schlichting