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

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.