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The use of trait-based approaches to understand ecological communities has increased in the past two decades because of their promise to preserve more information about community structure than taxonomic methods and their potential to connect community responses to subsequent effects of ecosystem functioning. Though trait-based approaches are a powerful tool for describing ecological communities, many important properties of commonly-used trait metrics remain unexamined. Previous work with simulated communities and trait distributions shows sensitivity of functional diversity measures to the number and correlation of traits used to calculate them, but these relationships have yet to be studied in actual plant communities with a realistic distribution of trait values, ecologically meaningful covariation of traits, and a realistic number of traits available for analysis. To address this gap, we used data from six grassland plant communities in Minnesota and New Mexico, USA to test how the number of traits and the correlation between traits used in the calculation of eight functional diversity indices impact the magnitude of functional diversity metrics in real plant communities. We found that most metrics were sensitive to the number of traits used to calculate them, but functional dispersion (FDis), kernel density estimation dispersion (KDE dispersion), and Rao’s quadratic entropy (Rao’s Q) maintained consistent rankings of communities across the range of trait numbers. Despite sensitivity of metrics to trait correlation, there was no consistent pattern between communities as to how metrics were affected by the correlation of traits used to calculate them. We recommend that future use of evenness metrics include sensitivity analyses to ensure results are robust to the number of traits used to calculate them. In addition, we recommend use of FDis, KDE dispersion, and Rao’s Q when ecologically applicable due to their ability to produce consistent rankings among communities across a range of the numbers of traits used to calculate them. 

Urban forests provide ecosystem services important for regulating climate, conserving biodiversity, and maintaining human well‐being. However, these forests vary in composition and physiological traits due to their unique biophysical and social contexts. This variation complicates assessing the functions and services of different urban forests. To compare the characteristics of the urban forest, we sampled the species composition and two externally sourced traits (drought tolerance and water‐use capacity) of tree and shrub species in residential yards, unmanaged areas, and natural reference ecosystems within six cities across the contiguous US. As compared to natural and unmanaged forests, residential yards had markedly higher tree and shrub species richness, were composed primarily of introduced species, and had more species with low drought tolerance. The divergence between natural and human‐managed areas was most dramatic in arid climates. Our findings suggest that the answer to the question of “what is an urban forest” strongly depends on where you look within and between cities. 

Abstract While the relationship between genetic diversity and plant productivity has been established for many species, it is unclear whether environmental conditions and biotic associations alter the nature of the relationship. To address this, we investigated the interactive effects of genotypic diversity, drought and mycorrhizal association on plant productivity and plant traits. Our mesocosm study was set up at the Konza Prairie Biological Research Station, located in the south of Manhattan, Kansas. Andropogon gerardii, the focal species for our study, was planted in two levels of genotypic richness treatment: monoculture or three-genotype polyculture. A rainout shelter was constructed over half of the experimental area to impose a drought and Thiophanate-methyl fungicide was used to suppress arbuscular mycorrhizal fungi in selected pots within each genotypic richness and drought treatment. Genotypic richness and mycorrhizal association did not affect above-ground biomass of A. gerardii. Drought differentially affected the above-ground biomass, the number of flowers and bolts of A. gerardii genotypes, and the biomass and the functional traits also differed for monoculture versus polyculture. Our results suggest that drought and genotypic richness can have variable outcomes for different genotypes of a plant species. 

The use of trait-based approaches to understand ecological communities has increased in the past two decades because of their promise to preserve more information about community structure than taxonomic methods and their potential to connect community responses to subsequent effects of ecosystem functioning. Though trait-based approaches are a powerful tool for describing ecological communities, many important properties of commonly-used trait metrics remain unexamined. Previous work in studies that simulate communities and trait distributions show consistent sensitivity of functional richness and evenness measures to the number of traits used to calculate them, but these relationships have yet to be studied in actual plant communities with a realistic distribution of trait values, ecologically meaningful covariation of traits, and a realistic number of traits available for analysis. Therefore, we propose to test how the number of traits used and the correlation between traits used in the calculation of functional diversity indices impacts the magnitude of eight functional diversity metrics in real plant communities. We will use trait data from three grassland plant communities in the US to assess the generality of our findings across ecosystems and experiments. We will determine how eight functional diversity metrics (functional richness, functional evenness, functional divergence, functional dispersion, kernel density estimation (KDE) richness, KDE evenness, KDE dispersion, Rao’s Q) differ based on the number of traits used in the metric calculation and on the correlation of traits when holding the number of traits constant. Without a firm understanding of how a scientist’s choices impact these metric, it will be difficult to compare results among studies with different metric parametrization and thus, limit robust conclusions about functional composition of communities across systems.