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Abstract Grasslands cover approximately a third of the Earth’s land surface and account for about a third of terrestrial carbon storage. Yet, we lack strong predictive models of grassland plant biomass, the primary source of carbon in grasslands. This lack of predictive ability may arise from the assumption of linear relationships between plant biomass and the environment and an underestimation of interactions of environmental variables. Using data from 116 grasslands on six continents, we show unimodal relationships between plant biomass and ecosystem characteristics, such as mean annual precipitation and soil nitrogen. Further, we found that soil nitrogen and plant diversity interacted in their relationships with plant biomass, such that plant diversity and biomass were positively related at low levels of nitrogen and negatively at elevated levels of nitrogen. Our results show that it is critical to account for the interactive and unimodal relationships between plant biomass and several environmental variables to accurately include plant biomass in global vegetation and carbon models. 

The tree diversity-productivity relationship is key to effective forest restoration and management; however, it remains unclear what role foliar chemical diversity and interactions between trees and their enemies play in driving this relationship. Trees produce chemical metabolites in their leaves that impact herbivory and pathogen infection. If trees alter the diversity of metabolites they produce when grown in more diverse communities, this could impact interactions with herbivores and pathogens. Ultimately, these tropic interactions with plant enemies, mediated by chemical diversity, could be important drivers of diversity-productivity relationships. Using a large-scale tree diversity experiment, we used a focal tree sampling design from 14 species across a gradient of tree species richness to assess the role of foliar chemicals and trophic interactions in the diversity-productivity relationship. We used untargeted metabolomics to measure foliar phytochemical diversity, monitored tree-enemy interactions, including foliar fungal pathogens, caterpillar communities, and deer browsing, and modelled their relationship to tree growth using path analysis. We unraveled significant evidence for top-down mediation of the diversity-productivity relationship driven primarily by herbivores rather than foliar pathogens, and contrasting effects of foliar chemical diversity on different enemy types. Individual trees growing in more diverse communities had higher phytochemical diversity and higher caterpillar richness, but lower leaf fungal pathogen richness. Leaf phytochemical diversity was positively associated with caterpillar richness and fungal pathogen richness, but negatively associated with browsing by white-tailed deer (Odocoileus virginianus). Path analysis revealed that phytochemical diversity, caterpillar richness, insect damage, and deer damage – but not foliar pathogens – all mediated positive indirect effects of tree richness on tree growth rate. Synthesis: We highlight the significant mediation of diversity-productivity relationships via contrasting effects of phytochemical diversity on plant-enemy interactions. Ultimately, our study underscores the importance of incorporating trophic interactions into biodiversity-ecosystem function studies. 

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 In our changing world, understanding plant community responses to global change drivers is critical for predicting future ecosystem composition and function. Plant functional traits promise to be a key predictive tool for many ecosystems, including grasslands; however, their use requires both complete plant community and functional trait data. Yet, representation of these data in global databases is sparse, particularly beyond a handful of most used traits and common species. Here we present the CoRRE Trait Data, spanning 17 traits (9 categorical, 8 continuous) anticipated to predict species’ responses to global change for 4,079 vascular plant species across 173 plant families present in 390 grassland experiments from around the world. The dataset contains complete categorical trait records for all 4,079 plant species obtained from a comprehensive literature search, as well as nearly complete coverage (99.97%) of imputed continuous trait values for a subset of 2,927 plant species. These data will shed light on mechanisms underlying population, community, and ecosystem responses to global change in grasslands worldwide. 

Abstract Research internships provide students with invaluable experience conducting independent research, contributing to larger research programs, and embedding in a professional scientific setting. These experiences increase student persistence in ecology and other science, technology, engineering, and mathematics (STEM) fields and promote the inclusion of students who lack opportunities at their home institutions and/or are from groups that are underrepresented in STEM. While many ecology internship programs were canceled during the 2020 COVID‐19 pandemic, others successfully adapted to offer virtual internships for the first time. Though different from what many researchers and students envision when they think of internships, virtual ecology internship programs can create more accessible opportunities and be just as valuable as in‐person opportunities when research programs and advisors develop virtual internships with intention and planning. Here, we highlight six ways to structure a virtual intern project, spanning a spectrum from purely computer‐based opportunities (e.g., digital data gathering, data analysis, or synthesis) to fully hands‐on research (e.g., sample processing or home‐based experiments). We illustrate examples of these virtual projects through a case study of the Smithsonian Environmental Research Center's 2020 Virtual Internship Program. Next, we provide 10 recommendations for effectively developing a virtual internship program. Finally, we end with ways that virtual internships can avoid the limitations of in‐person internships, as well as possible solutions to perceived pitfalls of virtual internships. While virtual internships became a necessity in 2020 due to COVID‐19, the development and continuation of virtual internships in future can be a valuable tool to add to the suite of existing internship opportunities, possibly further promoting diversity, equity, and inclusion in ecology and STEM. 

Abstract Causal effects of biodiversity on ecosystem functions can be estimated using experimental or observational designs — designs that pose a tradeoff between drawing credible causal inferences from correlations and drawing generalizable inferences. Here, we develop a design that reduces this tradeoff and revisits the question of how plant species diversity affects productivity. Our design leverages longitudinal data from 43 grasslands in 11 countries and approaches borrowed from fields outside of ecology to draw causal inferences from observational data. Contrary to many prior studies, we estimate that increases in plot-level species richness caused productivity to decline: a 10% increase in richness decreased productivity by 2.4%, 95% CI [−4.1, −0.74]. This contradiction stems from two sources. First, prior observational studies incompletely control for confounding factors. Second, most experiments plant fewer rare and non-native species than exist in nature. Although increases in native, dominant species increased productivity, increases in rare and non-native species decreased productivity, making the average effect negative in our study. By reducing the tradeoff between experimental and observational designs, our study demonstrates how observational studies can complement prior ecological experiments and inform future ones.