A method to detect subcommunities from multivariate spatial associations
A method to detect subcommunities from multivariate spatial associations
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DOI：
10.1111/2041-210x.12295
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发表时间：
2014-11
影响因子：
6.6
通讯作者：
Anton J. Flügge;S. Olhede;D. Murrell
中科院分区：
文献类型：
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作者：
Anton J. Flügge;S. Olhede;D. Murrell
物种很少在群落中随机分布，而是表现出由环境梯度和/或生物相互作用决定的空间结构。因此，对物种空间相关性的分析可能会揭示有助于形成这些模式的过程的信息。我们提出了一种多变量方法，利用所有物种对之间的空间关联来寻找其在研究区域内的分布呈正相关的物种亚群落。我们的方法从个体的模式开始，特别适合物种数量较多的群落，并给予稀有物种同等的权重。我们提出了一种方法来量化在物种独立的零模型下显著比预期更相关的子群落的最大数量。使用巴拿马巴罗科罗拉多岛(BCI)一块50公顷林地中乔木和灌木物种分布的数据，我们表明我们的方法可以用来构建具有生物意义的亚群落，这些亚群落与植物群落的空间结构有关。例如，我们根据环境梯度(如坡度)和不同的生物条件(如树冠空隙)，从紧随栖息地的亚群落构建空间地图。我们讨论了我们的方法的扩展和适应，这可能适用于其他类型的空间参考数据和其他生态群落。我们提出了以系统发育关系、生物学特性和环境变量作为协变量来解释亚群落的其他方法的建议，并注意到难以解释的亚群落可能暗示着值得进一步关注的物种和/或区域。
Species are seldom distributed at random across a community, but instead show spatial structure that is determined by environmental gradients and/or biotic interactions. Analysis of the spatial co‐associations of species may therefore reveal information on the processes that helped to shape those patterns. We propose a multivariate approach that uses the spatial co‐associations between all pairs of species to find subcommunities of species whose distribution in the study area is positively correlated. Our method, which begins with the patterns of individuals, is particularly well‐suited for communities with large numbers of species and gives rare species an equal weight. We propose a method to quantify a maximum number of subcommunities that are significantly more correlated than expected under a null model of species independence. Using data on the distribution of tree and shrub species from a 50 ha forest plot on Barro Colorado Island (BCI), Panama, we show that our method can be used to construct biologically meaningful subcommunities that are linked to the spatial structure of the plant community. As an example, we construct spatial maps from the subcommunities that closely follow habitats based on environmental gradients (such as slope) as well as different biotic conditions (such as canopy gaps). We discuss extensions and adaptations to our method that might be appropriate for other types of spatially referenced data and for other ecological communities. We make suggestions for other ways to interpret the subcommunities using phylogenetic relationships, biological traits and environmental variables as covariates and note that subcommunities that are hard to interpret may suggest groups of species and/or regions of the landscape that merit further attention.