The diversity of species combinations observable in sampling units reflects a species' uneven distribution and preference for specific abiotic and biotic conditions – a phenomenon most commonly expressed in terms of ecological assembly rules of plant communities and other sessile organisms (e.g. subtidal algae, invertebrates and coral reefs). We present comspat, a new R package that uses grid or transect data sets to measure the number of realized (observed) species combinations (NRC) and the Shannon diversity of realized species combinations (compositional diversity; CD) as a function of spatial scale. NRC and CD represent two measures from a model family developed by Pál Juhász-Nagy based on information theory. Classical Shannon diversity measures biodiversity based on the number and relative abundance of species, whereas the specific version of Shannon diversity presented here characterizes biodiversity and provides information on species coexistence relationships; both measures operate at fine-scale within the sampling unit or within the community. comspat offers two commonly applied null models, complete spatial randomness and random shift, to disentangle the textural, intraspecific and interspecific effects on the observed spatial patterns. Combined, these models assist users in detecting and interpreting spatial associations and inferring assembly mechanisms. Our open-sourced package provides a vignette that describes the method and reproduces the figures from this paper to help users contextualize and apply functions to their data.

中文翻译：

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comspat：使用物种组合分析社区内空间组织的 R 包

在采样单元中可观察到的物种组合的多样性反映了一个物种的不均匀分布和对特定非生物和生物条件的偏好——这种现象最常见于植物群落和其他固着生物（例如潮下藻类、无脊椎动物和珊瑚）的生态组装规则礁）。我们提出了 comspat，这是一个新的 R 包，它使用网格或样带数据集来测量已实现（观察到的）物种组合 (NRC) 的数量和已实现的物种组合的香农多样性（组成多样性；CD）作为空间尺度的函数。NRC 和 CD 代表 Pál Juhász-Nagy 基于信息论开发的模型系列中的两个度量。经典香农多样性根据物种的数量和相对丰度来衡量生物多样性，而这里提出的香农多样性的具体版本描述了生物多样性并提供了关于物种共存关系的信息；这两项措施都在抽样单位或社区内以精细的规模运作。comspat 提供了两种常用的空模型，完全空间随机性和随机移位，以解开对观察到的空间模式的纹理、种内和种间影响。结合起来，这些模型可帮助用户检测和解释空间关联并推断装配机制。我们的开源包提供了一个小插曲，描述了该方法并复制了本文中的数据，以帮助用户将函数上下文化并将函数应用于他们的数据。