R程序包“rdacca.hp”在生态学数据分析中的应用: 案例与进展

doi:10.17521/cjpe.2022.0314

植物生态学报 ›› 2023, Vol. 47 ›› Issue (1): 134-144.DOI: 10.17521/cjpe.2022.0314

所属专题：生态学研究的方法和技术

• 方法与技术 • 上一篇

R程序包“rdacca.hp”在生态学数据分析中的应用: 案例与进展

刘尧¹^,², 于馨³^,⁴, 于洋⁵, 胡文浩⁶, 赖江山¹^,³^,⁷^,^*()

¹南京林业大学生物与环境学院, 南京 210037
²厦门大学环境与生态学院, 福建厦门 361102
³中国科学院植物研究所植被与环境变化国家重点实验室, 北京 100093
⁴中国科学院大学, 北京 100049
⁵北京林业大学水土保持学院, 北京 100083
⁶浙江农林大学风景园林与建筑学院, 杭州 311300
⁷南京林业大学数量生态学研究中心, 南京 210037

收稿日期:2022-07-26 接受日期:2022-09-28 出版日期:2023-01-20 发布日期:2022-10-07
通讯作者: *赖江山(lai@njfu.edu.cn)
基金资助:
国家自然科学基金(32271551)

Application of “rdacca.hp” R package in ecological data analysis: case and progress

LIU Yao¹^,², YU Xin³^,⁴, YU Yang⁵, HU Wen-Hao⁶, LAI Jiang-Shan¹^,³^,⁷^,^*()

¹College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, China
²School of Environment and Ecology, Xiamen University, Xiamen, Fujian 361102, China
³State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
⁴University of Chinese Academy of Sciences, Beijing 100049, China
⁵School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China
⁶College of Landscape Architecture, Zhejiang A&F University, Hangzhou 311300, China
⁷Research Center of Quantitative Ecology, Nanjing Forestry University, Nanjing 210037, China

Received:2022-07-26 Accepted:2022-09-28 Online:2023-01-20 Published:2022-10-07
Contact: *LAI Jiang-Shan(lai@njfu.edu.cn)
Supported by:
National Natural Science Foundation of China(32271551)

摘要/Abstract

摘要：

定量评估不同变量对群落组成的贡献是群落生态学分析的热点问题。但在具体的分析情景中, 因子间的共线性与解释率的重叠对评估不同因子重要性造成了较大困难。基于这一问题, R程序包“rdacca.hp”通过引入层次分割法(HP)的理念, 在所有可能的模型子集下为各解释变量(或解释变量组)分配单独效应, 为典范分析中共线性解释变量的相对重要性评估提供了新的定量指标。目前, “rdacca.hp”包已经成为群落生态学分析的重要工具。为进一步促进用户对“rdacca.hp”包的理解与运用, 该文通过引入一个分析塑造甲螨(Oribatida)群落的重要环境和空间驱动因素的实例, 重点展示了使用该程序包进行典范分析的一般步骤。随后对近期应用“rdacca.hp”包开展分析的相关研究进行文献计量学分析, 结果表明该程序包自上线以来已被广泛用作解决生态学、环境科学及相关学科问题的基本定量框架。最后, 该文对“rdacca.hp”包的未来应用和升级进行了展望。总之, 该文旨在使国内学者们进一步加深对“rdacca.hp”包的认识和应用。

关键词: “rdacca.hp”包, 典范分析, 相对重要性, 层次分割, 群落组成

Abstract:

Quantitative estimation of the contribution of predictary variables to community composition is a hotspot in community ecology. However, multicollinearity and joint contributions among predictors make it difficult to estimate the importance of predictor in specific analysis scenarios. To address this issue, the “rdacca.hp” package provides a new quantitative indicator by introducing the concept of hierarchical partitioning (HP) to assign individual effects for individual predictors (or groups of predictors) across all possible model subsets. The package solves the problem of estimating the relative importance of predictors with multicollinearity in canonical analysis. The “rdacca.hp” package has become an important tool for community ecological analysis. To further promote users’ understanding and use of the “rdacca.hp” package, we demonstrate the general steps for using this package in canonical analysis with an example analyzing the important environmental and spatial drivers that shape the oribatid mites (Oribatida) community. Subsequently, we conduct a bibliometric analysis of recent studies using “rdacca.hp” package. The results show that, since its launch, the package has been widely used as a fundamental quantitative framework in ecology, environmental science and related disciplines. Finally, we discuss the further application and extension of the “rdacca.hp” package. In conclusion, this paper aims to advocate the understanding and application of the “rdacca.hp” package for domestic researchers.

Key words: “rdacca.hp” package, canonical analysis, relative importance, hierarchical partitioning, community composition

刘尧, 于馨, 于洋, 胡文浩, 赖江山. R程序包“rdacca.hp”在生态学数据分析中的应用: 案例与进展. 植物生态学报, 2023, 47(1): 134-144. DOI: 10.17521/cjpe.2022.0314

LIU Yao, YU Xin, YU Yang, HU Wen-Hao, LAI Jiang-Shan. Application of “rdacca.hp” R package in ecological data analysis: case and progress. Chinese Journal of Plant Ecology, 2023, 47(1): 134-144. DOI: 10.17521/cjpe.2022.0314

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2022.0314

https://www.plant-ecology.com/CN/Y2023/V47/I1/134

图/表 8

图1 以3组解释变量X1、X2和X3为例, 使用韦恩图概括了响应变量Y的总变差组成。A, 基于变量的顺序R2的方法, 根据分配的顺序, [a]、[b]和[c]分别为X1、X2和X3的条件效应。B, 基于变差分解和层次分割法, [a]、[b]和[c]分别为X1、X2和X3的边际效应, [d]、[e]和[f]分别为X1、X2和X3两两之间的共同效应, [g]为三者之间的共同效应, X1、X2和X3的单独效应分别为[a] + [d]/2 + [f]/2 + [g]/3、[b] + [d]/2 + [e]/2 + [g]/3以及[c] + [e]/2 + [f]/2 + [g]/3。残差[h]代表了未被X1、X2和X3解释的Y的变差部分。

Fig. 1 Venn diagram representing the variation composition of a response matrix Y regressed against three correlated predictors of X1, X2, and X3. A, According to the order of assignment, [a], [b] and [c] are the conditional effect of X1, X2 and X3, respectively. B, Based on variation partitioning and hierarchical partitioning, [a], [b] and [c] are the marginal effect of X1, X2 and X3, respectively; [d], [e] and [f] are the common effect of X1, X2 and X3, respectively; [g] is the common effect among X1, X2 and X3; the individual effect of X1, X2 and X3 can be expressed as [a] + [d]/2 + [f]/2 + [g]/3, [b] + [d]/2 + [e]/2 + [g]/3 and [c] + [e]/2 + [f]/2 + [g]/3. Residual [h] represents the fraction of Y that is not explained by X1, X2 or X3.

表1 “rdacca.hp”包的函数介绍

Table 1 Function of “rdacca.hp” package

函数 Function	介绍 Description
rdacca.hp	运行不限制解释变量数量的变差分解和层次分割, 计算每组解释变量对典范分析中解释变异(R²或校正R²)的边际、共同和单独效应Implements both variation partitioning and hierarchical partitioning in canonical analysis without limiting in the number of predictors/matrices of predictors. Output include the marginal, common, as well as individual effect of each (group) variables to total R² or adjusted R²
permu.hp	对解释变量的单独效应进行置换检验以评估其显著性 Implements the significance testing for individual contribution from hierarchical partitioning by permutation routine
plot.rdaccahp	基于“rdacca.hp”函数的输出, 绘制柱形图展示解释变量单独效应 Plot a bar plot of the individual contribution of predictors based on the output of rdacca.hp()

表2 R语言“vegan”包的甲螨多度及环境和空间因子数据集

Table 2 Oribatid mites abundance dataset with environmental and spatial variables in R package “vegan”

数据框 Data frame	介绍 Description
mite	物种多度矩阵, 包含70个采样点的共计35种甲螨形态种的个体计数 Species abundance matrix that contains the abundance of 35 oribatid mites morphospecies at 70 sampling sites
mite.env	环境因子矩阵, 包含70个采样点的共计5种环境变量: 基质密度、基质含水量、基质类型、灌丛密度和微地形特征 Environmental matrix that contains 5 environmental variables at 70 sampling sites, including substrate density, water content of the substrate, substrate type, shrub density, and microtopography
mite.pcnm	空间因子矩阵, 基于70个采样点的地理坐标, 通过邻体矩阵主坐标分析(PCNM)构建的特征向量, 反映了70个采样点的潜在空间结构 Spatial matrix, which is based on the geographic coordinates of 70 sampling sites and constructed by principal coordinates of neighbour matrices (PCNM) (Borcard & Legendre, 2002), represents the potential spatial structure of 70 sampling points

图2 5种环境因子对甲螨物种多度影响的相对重要性。变差分解和层次分割分析结果使用UpSet图来呈现。右侧点阵图中, 每行对应一个环境因子。对于每一列, 孤立黑点表示各环境因子的边际效应, 多点间连线表示这些环境因子间的共同效应, 各组分解释的变差百分比(来自变差分解)展示在上方柱形图中。左侧柱形图为各环境因子的单独效应(来自层次分割), 其值等同于该环境因子的边际效应加上与其他环境因子的共同效应的平均分配值。*, p < 0.05; ***, p < 0.001。

Fig. 2 UpSet matrix layout of variation partitioning and hierarchical partitioning results to show the relative importance of five environmental factors on oribatid mites abundance. In the dot-matrix plot on the right, each row corresponds to an environmental factor. For each column, the isolated black dot represents the marginal effect of each environmental factor, lines connecting multiple dots represent the common effect among these corresponding environmental factors, and the percentage of variation explained by each component (from variation partitioning) are shown in the top column diagram. Column diagram on the left shows individual effect of each environmental factor (from hierarchical partitioning), its value is equal to its marginal effect plus its average shared common effect with other environmental factors. *, p < 0.05; ***, p < 0.001.

图3 空间和环境因子对甲螨物种多度影响的相对重要性。变差分解和层次分割分析结果使用UpSet图来呈现。右侧点阵图中, 3列分别表示空间因子和环境因子的边际效应以及二者的共同效应(来自变差分解)。左侧柱形图表示二者的单独效应(来自层次分割)。***, p < 0.001。

Fig. 3 UpSet matrix layout of variation partitioning and hierarchical partitioning results to show the relative importance of spatial and environmental factors on oribatid mites abundance. In the dot-matrix plot on the right, three columns represent the marginal effect of spatial factors, the marginal effect of environmental factors, and the common effect between them, respectively (from variation partitioning). Column diagram on the left shows individual effect of spatial and environmental factors (from hierarchical partitioning). ***, p < 0.001.

图4 引用“rdacca.hp”包的论文发表时间及数量统计。虚线表示“rdacca.hp”包相关的方法学论文的正式发表时间。

Fig. 4 Statistics of published papers citing “rdacca.hp” package (publication time and number of published paper). Dashed line indicates the publication time of methodological paper related to “rdacca.hp” package.

图5 引用“rdacca.hp”包的论文所属的大类学科(红色)、论文中涉及的研究区域或环境类型(绿色)、典范分析的响应变量(蓝色)和解释变量类别(橙色)关键词的共现网络。连线表示关键词的共现关系, 节点大小和连线粗细代表了对应文献的数量。典范分析中解释变量的相对重要性分析均来自“rdacca.hp”包。

Fig. 5 Co-occurrence network of subject (red), study area or environment type (green), response variables (blue) and predictor variables (orange) key words according to published papers citing “rdacca.hp” package. Lines represent the co-occurrence relationship of keywords, node size and line width represent the number of related papers. All of the relative importance of predictors in canonical analysis are from “rdacca.hp” package.

图6 引用“rdacca.hp”包的论文中所涉及典范分析的响应变量类型及数量统计。CCA, 典范对应分析; db-RDA, 基于距离的冗余分析; RDA, 冗余分析。

Fig. 6 Statistics of published papers citing “rdacca.hp” package (type and number of response variables). CCA, canonical correspondence analysis; db-RDA, distance-based redundancy analysis; RDA, redundancy analysis.

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R程序包“rdacca.hp”在生态学数据分析中的应用: 案例与进展

Application of “rdacca.hp” R package in ecological data analysis: case and progress

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