Data from: Consensus RDA across dissimilarity coefficients for canonical ordination of community composition data

Understanding how habitat structures species assemblages in a community is one of the main goals of community ecology. To relate community patterns to particular factors defining habitat conditions, ecologists often use canonical ordinations such as canonical redundancy analysis (RDA). It is a common practice to use dissimilarity coefficients to perform canonical ordinations through distance-based RDA (db-RDA) or transformation-based RDA (tb-RDA). Dissimilarity coefficients are measures of resemblance where the information about species communities is condensed into a symmetric square matrix of dissimilarities among sites. In this paper we compared 16 of the most commonly used dissimilarity coefficients to evaluate if the species abundance distribution (SAD) of a community can be used to select an appropriate coefficient. Of these, 11 are designed to be used primarily with abundance data although they can also be used with presence-absence data, whereas five can only be applied to presence-absence data. Using simulations, we compared the explained variance of RDAs differing only by their coefficients to evaluate how the abundance patterns of communities influence coefficient choice. We found that coefficients are largely equivalent, independently of the community SAD. In light of these findings, we propose the consensus RDA method, a new canonical ordination procedure that performs a consensus of RDAs across several coefficients. This new method focuses on the common relations found by independent RDAs differing only by their dissimilarity coefficients; this ensures the absence of a coefficient-related bias when interpreting the canonical ordination result. Also, because in our simulations the presence-absence data were directly derived from the abundance data, we were able to evaluate if the information in presence-absence data was equivalent to that in abundance data. We found that although some information was lost by converting abundance data into presence-absence, both data formats may be complementary. When applying consensus RDA to abundance and presence-absence data independently, a more complete understanding and interpretation of the ecological patterns is obtained. An ecological example illustrating consensus RDA and the conclusions of our simulations is presented, using Carabidae data collected at the Ecosystem Management Emulating Natural Disturbances (EMEND) project in northwestern Alberta, Canada.

阐明生境如何塑造群落内的物种类群组成，是群落生态学（community ecology）的核心研究目标之一。为将群落格局与界定生境条件的特定驱动因子关联起来，生态学家常采用典范排序（canonical ordinations）方法，例如典范冗余分析（canonical redundancy analysis, RDA）。学界通常采用相异系数（dissimilarity coefficients），通过基于距离的典范冗余分析（distance-based RDA, db-RDA）或基于转换的典范冗余分析（transformation-based RDA, tb-RDA）开展典范排序。相异系数是一类群落相似性度量指标，可将物种群落信息压缩为样点间相异度的对称方阵。

本研究对比了16种最常用的相异系数，以探究能否通过群落的物种多度分布（species abundance distribution, SAD）筛选适配的相异系数。其中11种系数主要适配多度数据，虽也可用于物种有无数据，剩余5种则仅可应用于物种有无数据。本研究通过模拟实验，对比了仅在相异系数上存在差异的各类典范冗余分析的解释方差，以评估群落多度格局如何影响系数选择。研究结果表明，无论群落的物种多度分布类型如何，各类相异系数的表现大体一致。

基于上述结果，我们提出了共识典范冗余分析（consensus RDA）这一全新的典范排序方法：该方法对多种相异系数下的典范冗余分析结果进行共识整合，聚焦于仅通过相异系数区分的独立典范冗余分析所共有的关联关系，从而确保在解读典范排序结果时，不会引入与系数选择相关的偏差。此外，由于本研究的模拟实验中，物种有无数据直接由多度数据转换生成，我们得以评估物种有无数据携带的信息是否与多度数据等价。研究发现，尽管将多度数据转换为物种有无数据会丢失部分信息，但两类数据格式可互为补充。当分别对多度数据与物种有无数据应用共识典范冗余分析时，可获得对生态格局更全面的理解与解读。

本研究还提供了一则生态学应用实例：采用加拿大阿尔伯塔省西北部模拟自然干扰的生态系统管理项目（Ecosystem Management Emulating Natural Disturbances, EMEND）中采集的步甲科（Carabidae）物种数据，展示了共识典范冗余分析的应用流程与本研究模拟实验的结论。