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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 FROM AUTHOR]

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 FROM AUTHOR]

Grassland and other herbaceous communities cover significant portions of Earth's terrestrial surface and provide many critical services, such as carbon sequestration, wildlife habitat, and food production. Forecasts of global change impacts on these services will require predictive tools, such as process‐based dynamic vegetation models. Yet, model representation of herbaceous communities and ecosystems lags substantially behind that of tree communities and forests. The limited representation of herbaceous communities within models arises from two important knowledge gaps: first, our empirical understanding of the principles governing herbaceous vegetation dynamics is either incomplete or does not provide mechanistic information necessary to drive herbaceous community processes with models; second, current model structure and parameterization of grass and other herbaceous plant functional types limits the ability of models to predict outcomes of competition and growth for herbaceous vegetation. In this review, we provide direction for addressing these gaps by: (1) presenting a brief history of how vegetation dynamics have been developed and incorporated into earth system models, (2) reporting on a model simulation activity to evaluate current model capability to represent herbaceous vegetation dynamics and ecosystem function, and (3) detailing several ecological properties and phenomena that should be a focus for both empiricists and modelers to improve representation of herbaceous vegetation in models. Together, empiricists and modelers can improve representation of herbaceous ecosystem processes within models. In so doing, we will greatly enhance our ability to forecast future states of the earth system, which is of high importance given the rapid rate of environmental change on our planet. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Given the large and increasing amount of urban, suburban, and exurban land use on Earth, there is a need to accurately assess net primary productivity (NPP) of urban ecosystems. However, the heterogeneous and dynamic urban mosaic presents challenges to the measurement of NPP, creating landscapes that may appear more similar to a savanna than to the native landscape replaced. Studies of urban biomass have tended to focus on one type of vegetation (e.g., lawns or trees). Yet a focus on the ecology of the city should include the entire urban ecosystem rather than the separate investigation of its parts. Furthermore, few studies have attempted to measure urban aboveground NPP (ANPP) using field‐based methods. Most studies project growth rates from measurements of tree diameter to estimate annual ANPP or use remote sensing approaches. In addition, field‐based methods for measuring NPP do not address any special considerations for adapting such field methods to urban landscapes. Frequent planting and partial or complete removal of herbaceous and woody plants can make it difficult to accurately quantify increments and losses of plant biomass throughout an urban landscape. In this study, we review how ANPP of urban landscapes can be estimated based on field measurements, highlighting the challenges specific to urban areas. We then estimated ANPP of woody and herbaceous vegetation over a 15‐year period for Baltimore, MD, USA using a combination of plot‐based field data and published values from the literature. Baltimore's citywide ANPP was estimated to be 355.8 g m−2, a result that we then put into context through comparison with other North American Long‐Term Ecological Research (LTER) sites and mean annual precipitation. We found our estimate of Baltimore citywide ANPP to be only approximately half as much (or less) than ANPP at forested LTER sites of the eastern United States, and more comparable to grassland, oldfield, desert, or boreal forest ANPP. We also found that Baltimore had low productivity for its level of precipitation. We conclude with a discussion of the significance of accurate assessment of primary productivity of urban ecosystems and critical future research needs. [ABSTRACT FROM AUTHOR]

Feedbacks are an essential feature of resilient socio-economic systems, yet the feedbacks between biodiversity, ecosystem services and human wellbeing are not fully accounted for in global policy efforts that consider future scenarios for human activities and their consequences for nature. Failure to integrate feedbacks in our knowledge frameworks exacerbates uncertainty in future projections and potentially prevents us from realizing the full benefits of actions we can take to enhance sustainability. We identify six scientific research challenges that, if addressed, could allow future policy, conservation and monitoring efforts to quantitatively account for ecosystem and societal consequences of biodiversity change. Placing feedbacks prominently in our frameworks would lead to (i) coordinated observation of biodiversity change, ecosystem functions and human actions, (ii) joint experiment and observation programmes, (iii) more effective use of emerging technologies in biodiversity science and policy, and (iv) a more inclusive and integrated global community of biodiversity observers. To meet these challenges, we outline a five-point action plan for collaboration and connection among scientists and policymakers that emphasizes diversity, inclusion and open access. Efforts to protect biodiversity require the best possible scientific understanding of human activities, biodiversity trends, ecosystem functions and—critically—the feedbacks among them. [ABSTRACT FROM AUTHOR]

Understanding how global change drivers (GCDs) affect aboveground net primary production (ANPP) through time is essential to predicting the reliability and maintenance of ecosystem function and services in the future. While GCDs, such as drought, warming and elevated nutrients, are known to affect mean ANPP, less is known about how they affect inter-annual variability in ANPP. We examined 27 global change experiments located in 11 different herbaceous ecosystems that varied in both abiotic and biotic conditions, to investigate changes in the mean and temporal variability of ANPP (measured as the coefficient of variation) in response to different GCD manipulations, including resource additions, warming, and irrigation. From this comprehensive data synthesis, we found that GCD treatments increased mean ANPP. However, GCD manipulations both increased and decreased temporal variability of ANPP (24% of comparisons), with no net effect overall. These inconsistent effects on temporal variation in ANPP can, in part, be attributed to site characteristics, such as mean annual precipitation and temperature as well as plant community evenness. For example, decreases in temporal variability in ANPP with the GCD treatments occurred in wetter and warmer sites with lower plant community evenness. Further, the addition of several nutrients simultaneously increased the sensitivity of ANPP to interannual variation in precipitation. Based on this analysis, we expect that GCDs will likely affect the magnitude more than the reliability over time of ecosystem production in the future. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Intensification of the global hydrological cycle, ranging from larger individual precipitation events to more extreme multiyear droughts, has the potential to cause widespread alterations in ecosystem structure and function. With evidence that the incidence of extreme precipitation years (defined statistically from historical precipitation records) is increasing, there is a clear need to identify ecosystems that are most vulnerable to these changes and understand why some ecosystems are more sensitive to extremes than others. To date, opportunistic studies of naturally occurring extreme precipitation years, combined with results from a relatively small number of experiments, have provided limited mechanistic understanding of differences in ecosystem sensitivity, suggesting that new approaches are needed. Coordinated distributed experiments ( CDEs) arrayed across multiple ecosystem types and focused on water can enhance our understanding of differential ecosystem sensitivity to precipitation extremes, but there are many design challenges to overcome (e.g., cost, comparability, standardization). Here, we evaluate contemporary experimental approaches for manipulating precipitation under field conditions to inform the design of 'Drought-Net', a relatively low-cost CDE that simulates extreme precipitation years. A common method for imposing both dry and wet years is to alter each ambient precipitation event. We endorse this approach for imposing extreme precipitation years because it simultaneously alters other precipitation characteristics (i.e., event size) consistent with natural precipitation patterns. However, we do not advocate applying identical treatment levels at all sites - a common approach to standardization in CDEs. This is because precipitation variability varies >fivefold globally resulting in a wide range of ecosystem-specific thresholds for defining extreme precipitation years. For CDEs focused on precipitation extremes, treatments should be based on each site's past climatic characteristics. This approach, though not often used by ecologists, allows ecological responses to be directly compared across disparate ecosystems and climates, facilitating process-level understanding of ecosystem sensitivity to precipitation extremes. [ABSTRACT FROM AUTHOR]

Nitrogen (N) deposition and phosphorus (P) deposition due to pollution and land-use change are dramatically altering biogeochemical cycles. These altered nutrient inputs affect plant communities by generally increasing dominance and reducing diversity, as well as altering community variability (heterogeneity). Less well studied are the effects of changes in community variability on ecosystem functions, such as productivity, or the stability of those functions., Here, we use a twelve-year nutrient addition experiment in tallgrass prairie to determine the variability in community responses to N and P additions and link these responses to ecosystem productivity and stability. We added two levels of N and four levels of P in a fully factorial design to 25-m2 plots in native tallgrass prairie in north-eastern Kansas, USA. Each year percentage cover of each species was measured in June and August in a 1-m2 subplot of each plot, and annual net primary productivity was measured in two 0.1-m2 subplots in each plot at the end of each growing season., The addition of N and P together increased plant community variability across space (i.e. the replicates were significantly more different from each other in the N + P treatments than they were in the control treatment). We also found that the variability of the plant community within a single plot through time increased with the addition of N alone and N and P together. The highest level of both spatial and temporal variability occurred in plots with the highest level of nutrient addition (10 g m−2 of both N and P)., While we found no linkage between spatial variability of community composition and the spatial stability of productivity, the temporal stability of productivity decreased with increasing temporal plant community variability. Additionally, the ability to predict the productivity response to growing season precipitation, a key environmental variable, also decreased under higher temporal community variability., Synthesis. Using a 12-year nutrient addition experiment, we found that nutrient addition leads to both spatial and temporal community variability in mesic tallgrass prairie. The changes in community variability through time were directly related to ecosystem stability. While overall shifts in community structure in response to nutrient additions are important, the change in variability of local communities has significant implications for our ability to predict how patterns of biodiversity and ecosystem function will respond to a rapidly changing world. [ABSTRACT FROM AUTHOR]

Global change is increasing the frequency and magnitude of resource fluctuations (pulses) at multiple time-scales. According to the fluctuating resource availability hypothesis ( FRAH), susceptibility of an ecosystem to invasion (i.e. invasibility) is expected to increase whenever resource supply exceeds that which is utilized by native communities. Thus, global change has the potential to increase invasibility around the world., Here, we test the FRAH by adding seeds of a target invader grass species to a long-term climate change experiment manipulating precipitation pulse size in tallgrass prairie in Kansas, USA., Our experimental work yielded three important findings. First, contrary to predictions of the FRAH, invasibility was reduced with short time-scale resource pulses (intra-annual time-scale). Secondly, we found evidence to suggest that at inter-annual time-scales, the FRAH is supported. Wet years resulted in an increase in the number of established seedlings as well as the number of seedlings that persisted to the end of the season. Finally, we found that invasibility was positively related to native community richness and the density of individuals in the community suggesting that native communities facilitate establishment of invader species. Perhaps more importantly, results from this 10-year invasion study also show that resource availability drives invasion and that the biotic filters of plant community structure and diversity are secondary., Synthesis. Our findings suggest that intensification of precipitation regimes may enhance resistance to invasion at intra-annual time-scales, but will have opposing effects if precipitation regimes include more wet years. [ABSTRACT FROM AUTHOR]