Putting Things in Even Better Order: The Advantages of Canonical Correspondence Analysis

TLDR

Canonical Correspondence Analysis (CCA) is rapidly becoming the most widely used gradient analysis technique in ecology, building on Correspondence Analysis (CA) and addressing criticisms that have plagued CA and Detrended Correspondence Analysis. The study aims to test whether CCA suffers from the same defects as DCA by simulating data sets with properties that usually cause problems for DCA. The authors simulated data sets with properties that usually cause problems for DCA to evaluate CCA. Results indicate that CCA performs well with skewed species distributions, quantitative noise, unusual sampling designs, highly intercorrelated environmental variables, and incomplete knowledge of all factors determining species composition, and is immune to most of the problems of DCA.

Abstract

Canonical Correspondence Analysis (CCA) is quickly becoming the most widely used gradient analysis technique in ecology. The CCA algorithm is based upon Correspondence Analysis (CA), an indirect gradient analysis (ordination) technique. CA and a related ordination technique, Detrended Correspondence Analysis, have been criticized for a number of reasons. To test whether CCA suffers from the same defects, I simulated data sets with properties that usually cause problems for DCA. Results indicate that CCA performs quite well with skewed species distributions, with quantitative noise in species abundance data, with samples taken from unusual sampling designs, with highly intercorrelated environmental variables, and with situations where not all of the factors determining species composition are known. CCA is immune to most of the problems of DCA.