We examined the ability of eigenvalue tests to distinguish field-collected from random, assemblage structure data sets. Eight published time series of species abundances were used in the analysis, including data sets for: fishes, birds, mammals, stream benthos, and crabs. To test the efficacy of eigenvalue tests, we constructed 1000 randomly generated data sets for each real data set, whose means and variances were identical to the means and variances of the original data matrices. The data sets were then subjected to a principal components analysis (PCA) and eigenvalue tests used to identify significant eigenvalues for both correlation and covariance matrix solutions. We also examined the effects of: (1) number of species (= number of variables), (2) number of samples (= replication), and (3) variance structure, on the performance of the test. Using PCA's based on the correlation matrix and with sample sizes typically encountered in the field, the eigenvalue tests generally performed at the .05 level when alpha = .01. Slightly poorer results were obtained with the covariance matrix. Increasing the number of samples to at least three times the number of species generally gave alpha level coverage for an alpha level test (i.e., alpha = .05, .01). Increasing variance in the data set only affected test outcomes at levels of replication less than twice the number of species. We conclude that the eigenvalue tests can be used to detect patterns in PCA's of assemblage structure data, if the number of samples is at least three times the number of species and either a covariance or correlation matrix solution is used. It is assumed that these patterns represent ecologically meaningful patterns of variation.
