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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. 

Abstract Evolutionary correlations between chemical defense and protection by mutualist bodyguards have been long predicted, but tests of these patterns remain rare. We use a phylogenetic framework to test for evolutionary correlations indicative of trade-offs or synergisms between direct defense in the form of plant secondary metabolism and indirect defense in the form of leaf domatia, across 33 species in the wild grape genus, Vitis. We also performed a bioassay with a generalist herbivore to associate our chemical phenotypes with herbivore palatability. Finally, we tested whether defensive traits correlated with the average abiotic characteristics of each species’ contemporary range and whether these correlations were consistent with plant defense theory. We found a negative evolutionary correlation between domatia size and the diversity of secondary metabolites in Vitis leaf tissue across the genus, and also that leaves with a higher diversity and richness of secondary metabolites were less palatable to a generalist herbivore, consistent with a trade-off in chemical and mutualistic defense investment. Predictions from plant defense theory were not supported by associations between investment in defense phenotypes and abiotic variables. Our work demonstrates an evolutionary pattern indicative of a trade-off between indirect and direct defense strategies across the Vitis genus. 

The U.S. wine and grape industry loses $3B annually due to viral diseases including grapevine leafroll-associated virus complex 3 (GLRaV-3). Current detection methods are labor-intensive and expensive. GLRaV-3 has a latent period in which the vines are infected but do not display visible symptoms, making it an ideal model to evaluate the scalability of imaging spectroscopy-based disease detection. The NASA Airborne Visible and Infrared Imaging Spectrometer Next Generation was deployed to detect GLRaV-3 in Cabernet Sauvignon grapevines in Lodi, CA in September 2020. Foliage was removed from the vines as part of mechanical harvest soon after image acquisition. In September of both 2020 and 2021, industry collaborators scouted 317 hectares on a vine-by-vine basis for visible viral symptoms and collected a subset for molecular confirmation testing. Symptomatic grapevines identified in 2021 were assumed to have been latently infected at the time of image acquisition. Random forest models were trained on a spectroscopic signal of noninfected and GLRaV-3 infected grapevines balanced with synthetic minority oversampling of noninfected and GLRaV-3 infected grapevines. The models were able to differentiate between noninfected and GLRaV-3 infected vines both pre- and postsymptomatically at 1 to 5 m resolution. The best-performing models had 87% accuracy distinguishing between noninfected and asymptomatic vines, and 85% accuracy distinguishing between noninfected and asymptomatic + symptomatic vines. The importance of nonvisible wavelengths suggests that this capacity is driven by disease-induced changes to plant physiology. The results lay a foundation for using the forthcoming hyperspectral satellite Surface Biology and Geology for regional disease monitoring in grapevine and other crop species. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license . 

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.