Shifts in trait means and variances in North American tree assemblages:species richness patterns are loosely related to the functional space

One of the key hypothesized drivers of gradients in species richness is environmental filtering, where environmental stress limits which species from a larger species pool gain membership in a local community owing to their traits. Whereas most studies focus on small-scale variation in functional traits along environmental gradient, the effect of large-scale environmental filtering is less well understood. Furthermore, it has been rarely tested whether the factors that constrain the niche space limit the total number of coexisting species. We assessed the role of environmental filtering in shaping tree assemblages across North America north of Mexico by testing the hypothesis that colder, drier, or seasonal environments (stressful conditions for most plants) constrain tree trait diversity and thereby limit species richness. We assessed geographic patterns in trait filtering and their relationships to species richness pattern using a comprehensive set of tree range maps. We focused on four key plant functional traits reflecting major life history axes (maximum height, specific leaf area, seed mass, and wood density) and four climatic variables (annual mean and seasonality of temperature and precipitation). We tested for significant spatial shifts in trait means and variances using a null model approach. While we found significant shifts in mean species' trait values at most grid cells, trait variances at most grid cells did not deviate from the null expectation. Measures of environmental harshness (cold, dry, seasonal climates) and lower species richness were weakly associated with a reduction in variance of seed mass and specific leaf area. The pattern in variance of height and wood density was, however, opposite. These findings do not support the hypothesis that more stressful conditions universally limit species and trait diversity in North America. Environmental filtering does, however, structure assemblage composition, by selecting for certain optimum trait values under a given set of conditions.