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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. 

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 All multicellular organisms host a diverse microbiome composed of microbial pathogens, mutualists, and commensals, and changes in microbiome diversity or composition can alter host fitness and function. Nonetheless, we lack a general understanding of the drivers of microbiome diversity, in part because it is regulated by concurrent processes spanning scales from global to local. Global-scale environmental gradients can determine variation in microbiome diversity among sites, however an individual host’s microbiome also may reflect its local micro-environment. We fill this knowledge gap by experimentally manipulating two potential mediators of plant microbiome diversity (soil nutrient supply and herbivore density) at 23 grassland sites spanning global-scale gradients in soil nutrients, climate, and plant biomass. Here we show that leaf-scale microbiome diversity in unmanipulated plots depended on the total microbiome diversity at each site, which was highest at sites with high soil nutrients and plant biomass. We also found that experimentally adding soil nutrients and excluding herbivores produced concordant results across sites, increasing microbiome diversity by increasing plant biomass, which created a shaded microclimate. This demonstration of consistent responses of microbiome diversity across a wide range of host species and environmental conditions suggests the possibility of a general, predictive understanding of microbiome diversity. 

Plants are subject to tradeoffs among growth strategies such that adaptations for optimal growth in one condition can preclude optimal growth in another. Thus, we predicted that a plant species that responds positively to one global change treatment would be less likely than average to respond positively to another treatment, particularly for pairs of treatments that favor distinct traits. We examined plant species abundances in 39 global change experiments manipulating two or more of the following: CO2, nitrogen, phosphorus, water, temperature, or disturbance. Overall, the directional response of a species to one treatment was 13% more likely than expected to oppose its response to a another single-factor treatment. This tendency was detectable across the global dataset but held little predictive power for individual treatment combinations or within individual experiments. While tradeoffs in the ability to respond to different global change treatments exert discernible global effects, other forces obscure their influence in local communities. 

Summary Species dominance and biodiversity in plant communities have received considerable attention and characterisation. However, species codominance, while often alleged, is seldom defined or quantified. Codominance is a common phenomenon and is likely to be an important driver of community structure, ecosystem function and the stability of both. Here we review the use of the term ‘codominance’ and find inconsistencies in its use, suggesting that the scientific community currently lacks a universal understanding of codominance. We address this issue by: (1) qualitatively defining codominance as mostly shared abundance that is distinctively isolated within a subset of a community, and (2) presenting a novel metric for quantifying the degree to which relative abundances are shared among a codominant subset of plant species, while also accounting for the remaining species within a plant community. Using both simulated and real‐world data, we then demonstrate the process of applying the codominance metric to compare communities and to generate a quantitatively defensible subset of species to consider codominant within a community. We show that our metric effectively distinguishes the degree of codominance between four types of grassland ecosystems as well as simulated ecosystems with varying degrees of abundance sharing among community members. Overall, we make the case that increased research focusses on the conditions under which codominance occurs and the consequences for species coexistence, community structure and ecosystem function that would considerably advance the fields of community and ecosystem ecology.