Bias in community-weighted mean analysis of plant functional traits and species indicator values

One way to analyze the relationship between species attributes and sample attributes via the matrix of species composition is to calculate the community-weighted mean of species attributes (CWM) and relate it to sample attributes by correlation, regression or ANOVA. This weighted-mean approach is frequently used by vegetation ecologists to relate species attributes like plant functional traits or Ellenberg-like species indicator values to sample attributes like measured environmental variables, biotic properties, species richness or sample scores in ordination analysis. The problem with the weighted-mean approach is that, in certain cases, it yields biased results in terms of both effect size and P-values, and this bias is contingent upon the beta diversity of the species composition data. The reason is that CWM values calculated from samples of communities sharing some species are not independent of each other. This influences the number of effective degrees of freedom, which is usually lower than the actual number of samples, and the difference further increases with decreasing beta diversity of the data set. The discrepancy between the number of effective degrees of freedom and the number of samples in analysis turns into biased effect sizes and an inflated Type I error rate in those cases where the significance of the relationship is tested by standard tests, a problem which is analogous to analysis of two spatially autocorrelated variables. Consequently, results of studies using rather homogeneous (although not necessarily small) compositional data sets may be overly optimistic, and effect sizes of studies based on data sets differing by their beta diversity are not directly comparable. Here, I introduce guidelines on how to decide in which situation the bias is actually a problem when interpreting results, recognizing that there are several types of species and sample attributes with different properties and that ecological hypotheses commonly tested by the weighted-mean approach fall into one of three broad categories. I also compare available analytical solutions accounting for the bias (modified permutation test and sequential permutation test using the fourth-corner statistic) and suggest rules for their use.

Analysis of Species Indicator Values in Response to Spatial Environmental Variation of Differently Managed Agro-habitats

This paper investigates species indicator values in response to spatial gradients of environmental indices (light, L; moisture, F; nitrogen, N; temperature, T) in different agro-habitats (crop fields and their boundaries of intensive/conventional farming, IF; organic farming, OF) of Lithuania. All plant species were classified according to indicator values of the Ellenberg scale of general abiotic environmental factors (light, moisture, nitrogen, temperature) available for Central Europe. Multiple Correspondence Analysis was applied to analyze the patterns of relationships between species indicator values and environmental conditions in six different agro-habitats. Variation of N-values (ranging from 2 to 9 and x point) was observed to be the highest between ecological gradients, thus indicating wide spatial dispersion of soil N deposition in the habitats. The presence of particular plant species with medium indicator values (L5-L6, F4-F5, N5-N6, T4-T5) suggests that IF crop habitats are favored for establishment of mezophytes. Crop and margin habitats in OF agro-habitats were found to possess a wider environmental gradient, ensuring higher biodiversity.

Picea abiessite index prediction by environmental factors and understorey vegetation: a two-scale approach based on survey databases

Relationships between site index, environmental variables, and understorey vegetation were examined for Norway spruce (Picea abies (L.) Karst.) in the eastern part of France. The study area concerns all the native range of Norway spruce in France and the northeastern plains. The analysis is based on 2087 plots from the French National Forest Inventory database. The data measured on each plot cover topography, soil, geology, and vegetation. Additional environmental variables were estimated using two methods: climatic data estimated from a climatic model developed by Météo-France (AURELHY), and nutritional variables predicted from vegetation data and species indicator values. General linear model regression was used to predict site index as a function of environmental variables. The best model explains 64% of the site index variance and involves eight variables (elevation, mountain zone, topographic concavity, proportion of plot area occupied by rock outcrop, rock type, soil depth, pH, and C/N ratio). The two main results of this study are (i) the combination of large databases allowed the study of soil–site relationships and construction of a pertinent model, which covers a wide range of ecological conditions, and (ii) vegetation was found to be relevant to separate the effect of acidity from those of nitrogen nutrition on Norway spruce productivity.

Trends and interrelationships in boreal wetland vegetation

Multivariate statistical methods were used to examine trends and interrelationships in 132 wetland stands at the southern edge of the boreal forest near Elk Lake, Ont., Canada. A total of nine vegetation types and seven species groups were recognized using cluster analysis. Nonmetric multidimensional scaling ordination of the stands indicated the underlying importance of nutrient status to the development of trends in vegetational variation. However, other factors such as the nature of the substratum, degree and periodicity of flooding, drainage, and water table level also appeared to be important. Analysis of the correspondence between vegetation types and species ecological groups indicated a trend toward the development of one-to-one relationships, suggesting that boreal wetlands may best be described as a series of relatively discrete communities. It is also suggested that species indicator values may be useful in characterizing boreal wetland stands.