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Random species loss has been shown experimentally to reduce ecosystem function, sometimes more than other anthropogenic environmental changes. Yet, controversy surrounds the importance of this finding for natural systems where species loss is non‐random. We compiled data from 16 multi‐year experiments located at a single native tallgrass prairie site. These experiments included responses to 11 anthropogenic environmental changes, as well as non‐random biodiversity loss either the removal of uncommon/rare plant species or the most common (dominant) species. As predicted by the mass ratio hypothesis, loss of a dominant species had large impacts on productivity that were comparable to other anthropogenic drivers. In contrast, the loss of uncommon/rare species had small effects on productivity despite having the largest effects on species richness. The anthropogenic drivers that had the largest effects on productivity nitrogen, irrigation, and fire experienced not only loss of species but also significant changes in the abundance and identity of dominant species. Synthesis. These results suggest that mass ratio effects, rather than species loss per se, are an important determinant of ecosystem function with environmental change. The mass ratio effects, rather than richness loss, determine ecosystem function with environmental change.

Univariate and multivariate methods are commonly used to explore the spatial and temporal dynamics of ecological communities, but each has limitations, including oversimplification or ion of communities. Rank abundance curves (RACs) potentially integrate these existing methodologies by detailing species‐level community changes. Here, we had three goals: first, to simplify analysis of community dynamics by developing a coordinated set of R functions, and second, to demystify the relationships among univariate, multivariate, and RACs measures, and examine how each is influenced by the community parameters as well as data collection methods. We developed new functions for studying temporal changes and spatial differences in RACs in an update to the R package library(“codyn”), alongside other new functions to calculate univariate and multivariate measures of community dynamics. We also developed a new approach to studying changes in the shape of RAC curves. The R package update presented here increases the accessibility of univariate and multivariate measures of community change over time and difference over space. Next, we use simulated and real data to assess the RAC and multivariate measures that are output from our new functions, studying (1) if they are influenced by species richness and evenness, temporal turnover, and spatial variability and (2) how the measures are related to each other. Lastly, we explore the use of the measures with an example from a long‐term nutrient addition experiment. We find that the RAC and multivariate measures are not sensitive to species richness and evenness and that all the measures detail unique aspects of temporal change or spatial differences. We also find that species reordering is the strongest correlate of a multivariate measure of compositional change and explains most community change observed in long‐term nutrient addition experiment. Overall, we show that species reordering is potentially an understudied determinant of community changes over time or differences between treatments. The functions developed here should enhance the use of RACs to further explore the dynamics of ecological communities.

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. Drivers responsible for plant microbiome variation between sites remain elusive. Here, the authors test how soil nutrient addition and herbivore exclusion affect plant microbiota in grasslands worldwide, showing that microbiota diversity responded to environmental variation similarly across sites.

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.

Ecological communities are being impacted by global change worldwide. Experiments are a powerful tool to understand how global change will impact communities by comparing control and treatment replicates. Communities consist of multiple species, and their associated abundances make multivariate methods an effective approach to study community compositional differences between control and treated replicates. Dissimilarity metrics are a commonly employed multivariate measure of compositional differences; however, while highly informative, dissimilarity metrics do not elucidate the specific ways in which communities differ. Integrating two multivariate methods, dissimilarity metrics and rank abundance curves (RACs), have the potential to detect complex differences based on dissimilarity metrics and detail the how these differences came about through differences in richness, evenness, species ranks, or species identity. Here we use a database of 106 global change experiments located in herbaceous ecosystems and explore how patterns of ordinations based on dissimilarity metrics relate to RAC‐based differences. We find that combining dissimilarity metrics alongside RAC‐based measures clarifies how global change treatments are altering communities. We find that when there is no difference in community composition (no distance between centroids of control and treated replicates), there are rarely differences in species ranks or species identities and more often differences in richness or evenness alone. In contrast, when there are differences between centroids of control and treated replicates, this is most often associated with differences in ranks either alone or co‐occurring with differences in richness, evenness, or species identities. We suggest that integrating these two multivariate measures of community composition results in a deeper understanding of how global change impacts communities.

Human activities are enriching many of Earth’s ecosystems with biologically limiting mineral nutrients such as nitrogen (N) and phosphorus (P). In grasslands, this enrichment generally reduces plant diversity and increases productivity. The widely demonstrated positive effect of diversity on productivity suggests a potential negative feedback, whereby nutrient-induced declines in diversity reduce the initial gains in productivity arising from nutrient enrichment. In addition, plant productivity and diversity can be inhibited by accumulations of dead biomass, which may be altered by nutrient enrichment. Over longer time frames, nutrient addition may increase soil fertility by increasing soil organic matter and nutrient pools. We examined the effects of 5–11 yr of nutrient addition at 47 grasslands in 12 countries. Nutrient enrichment increased aboveground live biomass and reduced plant diversity at nearly all sites, and these effects became stronger over time. We did not find evidence that nutrient-induced losses of diversity reduced the positive effects of nutrients on biomass; however, nutrient effects on live biomass increased more slowly at sites where litter was also increasing, regardless of plant diversity. This work suggests that short-term experiments may underestimate the long term nutrient enrichment effects on global grassland ecosystems.

Anthropogenic nutrient enrichment is driving global biodiversity decline and modifying ecosystem functions. Theory suggests that plant functional types that fix atmospheric nitrogen have a competitive advantage in nitrogen-poor soils, but lose this advantage with increasing nitrogen supply. By contrast, the addition of phosphorus, potassium, and other nutrients may benefit such species in low-nutrient environments by enhancing their nitrogen-fixing capacity. We present a global-scale experiment confirming these predictions for nitrogen-fixing legumes (Fabaceae) across 45 grasslands on six continents. Nitrogen addition reduced legume cover, richness, and biomass, particularly in nitrogen-poor soils, while cover of non–nitrogen-fixing plants increased. The addition of phosphorous, potassium, and other nutrients enhanced legume abundance, but did not mitigate the negative effects of nitrogen addition. Increasing nitrogen supply thus has the potential to decrease the diversity and abundance of grassland legumes worldwide regardless of the availability of other nutrients, with consequences for biodiversity, food webs, ecosystem resilience, and genetic improvement of protein-rich agricultural plant species.

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.

Studies of biodiversity and ecosystem function (BEF) have long focused on the role of nitrogen (N)-fixing legumes as a functional group that occupies a distinct and important niche relative to other plants. Because of their relationship with N-fixing rhizobial bacteria, these legumes access a different pool of N than other plants and therefore directly contribute to increases in productivity and N-cycling. Despite their recognized importance in the BEF literature, the field has not moved far beyond investigating the presence/absence of the legume functional group in species mixtures. Here, we synthesize existing information on how the diversity (species richness and functional diversity) of both legumes and the rhizobia that they host impact ecosystem functions, such as nitrogen fixation and primary productivity. We also discuss the often-overlooked reciprocal direction of the BEF relationship, whereby ecosystem function can influence legume and rhizobial diversity. We focus on BEF mechanisms of selection, complementarity, facilitation, competitive interference, and dilution effects to explain how diversity in the legume–rhizobia mutualism can have either positive or negative effects on ecosystem function—mechanisms that can operate at scales from rhizobial communities affecting individual legume functions to legume communities affecting landscape-scale ecosystem functions. To fully understand the relationship between biodiversity and ecosystem function, we must incorporate the full diversity of this mutualism and its reciprocal relationship with ecosystem function into our evolving BEF framework.

Urbanization has increased wealth disparity within the United States, impacting the urban landscape and species interactions. In particular, the interactions between street trees and the arthropod communities that live among them may be modified by both human population densities across urban to suburban locations, as well as income levels within these areas. We examined the effect of land use type (urban vs. suburban) and median household income on variation in leaf damage and arthropod abundance of red maples (Acer rubrum) in the District of Columbia metropolitan region. We compared these levels of leaf damage to rates observed in a nearby temperate deciduous forest. We predicted leaf damage would be positively correlated with urbanization (forested < suburban < urban) and negatively correlated with neighborhood income level (low > medium > high). Instead, we observed higher levels of leaf damage on trees in the forest environment compared to the urban and suburban areas. Leaves from urban medium and high-income areas were less likely to exhibit herbivore damage than those from suburban areas. Of the leaves with damage, those in urban high-income and suburban low-income areas exhibited the most leaf area missing. These trends may be related to specific factors associated with urbanization and income level, such as impervious surface coverage and tree coverage. This study highlights differences in biotic interactions across individual neighborhoods and the importance of including socio-economic variables when examining species interactions in urban environments.