The problem of pattern and scale is a central challenge in ecology. In community ecology, an important scale is that at which we aggregate species to define our units of study, such as aggregation of “nitrogen fixing trees” to understand patterns in carbon sequestration. With the emergence of massive community ecological data sets, there is a need to objectively identify the scales for aggregating species to capture well‐defined patterns in community ecological data. The phylogeny is a scaffold for identifying scales of species‐aggregation associated with macroscopic patterns. Phylofactorization was developed to identify phylogenetic scales underlying patterns in relative abundance data, but many ecological data, such as presence‐absences and counts, are not relative abundances yet may still have phylogenetic scales capturing patterns of interest. Here, we broaden phylofactorization to a graph‐partitioning algorithm identifying phylogenetic scales in community ecological data. As a graph‐partitioning algorithm, phylofactorization connects many tools from data analysis to phylogenetically informed analyses of community ecological data. Two‐sample tests identify five phylogenetic factors of mammalian body mass which arose during the K‐Pg extinction event, consistent with other analyses of mammalian body mass evolution. Projection of data onto coordinates connecting the phylogeny and graph‐partitioning algorithm yield a phylogenetic principal components analysis which refines our understanding of the major sources of variation in the human gut microbiome. These same coordinates allow generalized additive modeling of microbes in Central Park soils, confirming that a large clade of Acidobacteria thrive in neutral soils. The graph‐partitioning algorithm extends to generalized linear and additive modeling of exponential family random variables by phylogenetically constrained reduced‐rank regression or stepwise factor contrasts. All of these tools can be implemented with the R package phylofactor.

中文翻译：

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植物分解：一种图形划分算法，用于识别生态数据的系统发生尺度

模式和规模问题是生态学中的主要挑战。在社区生态学中，一个重要的尺度是我们聚集物种以定义研究单位的尺度，例如聚集“固氮树”以了解碳固存的模式。随着大量社区生态数据集的出现，有必要客观地确定聚集物种的规模，以捕获社区生态数据中明确定义的模式。系统发育是一种用于识别与宏观模式相关的物种聚集规模的支架。进行系统分解是为了确定相对丰度数据中潜在模式的系统发生尺度，但许多生态数据（例如存在与否）不是相对丰度，但仍可能具有捕获目标模式的系统发生尺度。在这里，我们将系统分解扩展到一种图形分区算法，该算法可识别社区生态数据中的系统发生规模。系统分解是一种图划分算法，它连接了许多工具，从数据分析到社区生态数据的系统发育信息分析。两项样本测试确定了在K-Pg灭绝事件中出现的五个哺乳动物体重的系统发育因素，这与对哺乳动物体重演变的其他分析一致。将数据投影到连接系统发育和图划分算法的坐标上会产生系统发育的主成分分析，这有助于我们了解人类肠道微生物组主要变异来源。这些相同的坐标可以对中央公园土壤中的微生物进行广义的附加建模，证实大量的酸性细菌在中性土壤中生长。图划分算法通过系统发育约束的降秩回归或逐步因子对比，扩展到指数家族随机变量的广义线性和加性建模。所有这些工具都可以使用R包系统因子来实现。