Combining the fourth‐corner and the RLQ methods for assessing trait responses to environmental variation

TLDR

Assessing trait responses to environmental gradients requires simultaneous analysis of species distribution, environmental characteristics, and species traits, and the fourth‑corner and RLQ methods are two alternatives that test trait‑environment relationships, differing in outputs: RLQ provides multivariate ordination scores, while the fourth‑corner tests individual trait‑environment pairs. We illustrate how combining these complementary methods can enhance ecological knowledge and improve the study of trait‑environment relationships. We briefly describe each method and then apply them to real ecological data to compare their outputs and demonstrate the benefits of combining them. The application to real data shows distinct outputs and suggests that combining the methods yields additional insights into trait‑environment relationships.

Abstract

Assessing trait responses to environmental gradients requires the simultaneous analysis of the information contained in three tables: L (species distribution across samples), R (environmental characteristics of samples), and Q (species traits). Among the available methods, the so-called fourth-corner and RLQ methods are two appealing alternatives that provide a direct way to test and estimate trait-nvironment relationships. Both methods are based on the analysis of the fourth-corner matrix, which crosses traits and environmental variables weighted by species abundances. However, they differ greatly in their outputs: RLQ is a multivariate technique that provides ordination scores to summarize the joint structure among the three tables, whereas the fourth-corner method mainly tests for individual trait-environment relationships (i.e., one trait and one environmental variable at a time). Here, we illustrate how the complementarity between these two methods can be exploited to promote new ecological knowledge and to improve the study of trait-environment relationships. After a short description of each method, we apply them to real ecological data to present their different outputs and provide hints about the gain resulting from their combined use.