Kraft et al. (Reports, 24 October 2008, p. 580) used a variety of metrics describing the distribution of functional traits within a tropical forest community to demonstrate simultaneous environmental filtering and niche differentiation. We discuss how these results could have arisen from sampling design and statistical assumptions, suggesting alternative approaches that could better resolve these questions.K raft et al.(1) used the distribution of functional traits in a diverse tropical forest community to test hypotheses of community assembly. The study focused on six traits that drive diverse ecological functions. By comparing metrics of observed distributions of each trait with those expected under different null models, the authors showed evidence for both classical niche differentiation and environmental filtering at the scale of 20-by 20-m quadrats. Relative to null expectations, environmental filtering was indicated by shifting trait means, decreased trait range, and variance within quadrats, whereas differentiation was indicated by overdispersion, measured by standard deviation (SD) of nearestneighbor distance in trait space, and kurtosis of the within-quadrat trait distribution. Combined, these tests were used to argue for the important role of stabilizing processes in the maintenance of biodiversity in a hyperdiverse tropical forest. This approach represents movement toward a more comprehensive use of functional traits in testing processes of community assembly. Nevertheless, there are some subtle problems in the Kraft et al. analysis that we expand upon here.First, Kraft et al.(1) inappropriately tested for both environmental filtering and niche differentiation using null models that select randomly from the entire species pool. If environmental filtering limits the range of traits of species that can occur in a given quadrat, an accurate test of niche differentiation within quadrats must sample only from species that fall between the minimum and maximum traits present in a quadrat. The larger the available trait space, the larger the possible range of nearest-neighbor distances in trait space, and hence the bigger the SD of nearest-neighbor distances will be under a random draw from that available trait space. Thus, if limitations on the range of trait values in each quadrat due to habitat filtering are not accounted for in the null trait distribution used in the niche differentiation test, one might artificially conclude that the observed SD of nearest-neighbor distances is smaller than expected by chance and that niche differentiation is occurring. Similar problems might arise in the comparison of kurtosis values.Second, Kraft et al. ignored intraspecific variation. Due to the obvious difficulty of collecting trait data for all individuals, the authors targeted outer canopy leaves of trees in the 1-to 5-cm diameter at breast height (dbh) size class growing under closed canopy that were readily accessed from the ground, leading to a collection of 2 to 5 leaves from 1 to 20 individuals for most of t...