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

doi:10.17521/cjpe.2022.0314

Abstract

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

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[J]. Chin J Plant Ecol, 2023, 47(1): 134-144.

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URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2022.0314

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

Figures/Tables 8

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.

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()

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

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.

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.

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.

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.

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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