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ABSTRACT Biodiversity promotes ecosystem productivity and stability, positive impacts that often strengthen over time. But ongoing global changes such as rising atmospheric carbon dioxide (CO₂) levels and anthropogenic nitrogen (N) deposition may modulate the impact of biodiversity on ecosystem productivity and stability over time. Using a quarter‐century grassland biodiversity‐global change experiment we show that diversity increasingly enhanced productivity over time irrespective of global change treatments. In contrast, the positive influence of diversity on ecosystem stability strengthened over time under ambient conditions but weakened to varying degrees under global change treatments, largely driven by a greater reduction in species asynchrony under global changes. Thus, over 25 years, CO₂and N enrichment gradually eroded some of the positive effects of biodiversity on ecosystem stability. As elevated CO₂, N eutrophication, and biodiversity loss increasingly co‐occur in grasslands globally, our results raise concerns about their potential joint detrimental effects on long‐term grassland stability. 

Abstract Plant disease often increases with N, decreases with CO₂, and increases as biodiversity is lost (i.e., the dilution effect). Additionally, all these factors can indirectly alter disease by changing host biomass and hence density-dependent disease transmission. Yet over long periods of time as communities undergo compositional changes, these biomass-mediated pathways might fade, intensify, or even reverse in direction. Using a field experiment that has manipulated N, CO₂, and species richness for over 20 years, we compared severity of a specialist rust fungus (Puccinia andropogonis) on its grass host (Andropogon gerardii) shortly after the experiment began (1999) and twenty years later (2019). Between these two sampling periods, two decades apart, we found that disease severity consistently increased with N and decreased with CO₂. However, the relationship between diversity and disease reversed from a dilution effect in 1999 (more severe disease in monocultures) to an amplification effect in 2019 (more severe disease in mixtures). The best explanation for this reversal centered on host density (i.e., aboveground biomass), which was initially highest in monoculture, but became highest in mixtures two decades later. Thus, the diversity-disease pattern reversed, but disease consistently increased with host biomass. These results highlight the consistency of N and CO₂as drivers of plant disease in the Anthropocene and emphasize the critical role of host biomass—despite potentially variable effects of diversity—for relationships between biodiversity and disease. 

Measuring the chemical traits of leaf litter is important for understanding plants’ influence on nutrient cycles, including through nutrient resorption and litter decomposition, but conventional leaf trait measurements are often destructive and labor-intensive. Here, we develop and evaluate the performance of partial least-squares regression models that use reflectance spectra of intact or ground leaves to estimate leaf litter traits, including carbon and nitrogen concentration, carbon fractions, and leaf mass per area (LMA). Our analyses included more than 300 samples of senesced foliage from 11 species of temperate trees, including both needleleaf and broadleaf species. Across all samples, we could predict each trait with moderate-to-high accuracy from both intact-leaf litter spectra (validation R² = 0.543–0.941; %root mean squared error (RMSE) = 7.49–18.5) and ground-leaf litter spectra (validation R² = 0.491–0.946; %RMSE = 7.00–19.5). Notably, intact-leaf spectra yielded better predictions of LMA. Our results support the feasibility of building models to estimate multiple chemical traits from leaf litter of a range of species. In particular, intact-leaf spectral models allow non-destructive trait estimation in a matter of seconds, which could enable researchers to measure the same leaves over time in studies of nutrient resorption. 

Abstract Plant functional groups (FGs) differ in their response to global changes, although species within those groups also vary in such responses. Both species and FG responses to global change are likely influenced by species interactions such as inter‐specific competition and facilitation, which are prevalent in species mixtures but not monocultures. As most studies focus on responses of plants growing in either monocultures or mixtures, but rarely both, it remains unclear how interspecific interactions in diverse ecological communities, especially among species in different FGs, modify FG responses to global changes. To address these issues, we leveraged data from a 16‐species, 24‐year perennial grassland experiment to examine plant FG biomass responses to atmospheric CO₂, and N inputs at different planted diversity. FGs differed in their responses to N and CO₂treatments in monocultures. Such differences were amplified in mixtures, where N enrichment strongly increased C3 grass success at ambient CO₂and C4 grass success at elevated CO₂. Legumes declined with N enrichment in mixtures at both CO₂levels and increased with elevated CO₂in the initial years of the experiment. Our results suggest that previous studies that considered responses to global changes in monocultures may underestimate biomass changes in diverse communities where interspecific interactions can amplify responses. Such effects of interspecific interactions on responses of FGs to global change may impact community composition over time and consequently influence ecosystem functions. 

Abstract We introduce a new “ecosystem‐scale” experiment at the Cedar Creek Ecosystem Science Reserve in central Minnesota, USA to test long‐term ecosystem consequences of tree diversity and composition. The experiment—the largest of its kind in North America—was designed to provide guidance on forest restoration efforts that will advance carbon sequestration goals and contribute to biodiversity conservation and sustainability.The new Forest and Biodiversity (FAB2) experiment uses native tree species in varying levels of species richness, phylogenetic diversity and functional diversity planted in 100 m²and 400 m²plots at 1 m spacing, appropriate for testing long‐term ecosystem consequences. FAB2 was designed and established in conjunction with a prior experiment (FAB1) in which the same set of 12 species was planted in 16 m²plots at 0.5 m spacing. Both are adjacent to the BioDIV prairie‐grassland diversity experiment, enabling comparative investigations of diversity and ecosystem function relationships between experimental grasslands and forests at different planting densities and plot sizes.Within the first 6 years, mortality in 400 m²monoculture plots was higher than in 100 m²plots. The highest mortality occurred inTilia americanaandAcer negundomonocultures, but mortality for both species decreased with increasing plot diversity. These results demonstrate the importance of forest diversity in reducing mortality in some species and point to potential mechanisms, including light and drought stress, that cause tree mortality in vulnerable monocultures. The experiment highlights challenges to maintaining monoculture and low‐diversity treatments in tree mixture experiments of large extent.FAB2 provides a long‐term platform to test the mechanisms and processes that contribute to forest stability and ecosystem productivity in changing environments. Its ecosystem‐scale design, and accompanying R package, are designed to discern species and lineage effects and multiple dimensions of diversity to inform restoration of ecosystem functions and services from forests. It also provides a platform for improving remote sensing approaches, including Uncrewed Aerial Vehicles (UAVs) equipped with LiDAR, multispectral and hyperspectral sensors, to complement ground‐based monitoring. We aim for the experiment to contribute to international efforts to monitor and manage forests in the face of global change. 

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.