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Abstract QuestionsGrasslands provide important provisioning services worldwide and their management has consequences for these services. Management intensification is a widespread land‐use change and has accelerated across North America to meet rising demands on productivity, yet its impact on the relationship between plant diversity and productivity is still unclear. Here, we investigated the relationship between plant diversity and grassland productivity across nine ecoclimatic domains of the continental United States. We also tested the effect of management intensification on diversity and productivity in four case studies. MethodsWe acquired remotely sensed gross primary productivity data (GPP, 1986–2018) and plant diversity data measured at different spatial scales (1, 10, 100, 400 m²), as well as climate variables including the Palmer drought index from two ecological networks. We used general linear mixed models to relate GPP to plant diversity across sites. For the case study analysis, we used linear mixed models to relate plant diversity to management intensity, and tested if the management intensity influenced the relationship between GPP (mean and temporal variation) and drought. ResultsAcross all sites, we observed positive relationships among species richness, productivity, and the temporal stability of mean annual biomass production. These relationships were not affected by the scale at which species richness was observed. In three out of the four case studies, we observed that management effects on species richness were only significant at broader scales (i.e., ≥10 m²) with no clear effect found at the commonly used 1‐m²quadrat scale. In one case study, species‐poor, intensively managed pastures presented the highest productivity but were more sensitive to dry conditions than less intensified pastures. However, in other case studies, we did not observe significant effects of management intensity on the magnitude or stability of productivity. ConclusionsGeneralization across studies may be difficult and require the development of intensification indices general enough to be applied across diverse management strategies in grazilands. Understanding how management intensification affects grassland productivity will inform the development of sustainable intensification strategies. 

Abstract The decision to establish a network of researchers centers on identifying shared research goals. Ecologically specific regions, such as the USA’s National Ecological Observatory Network’s (NEON’s) eco-climatic domains, are ideal locations by which to assemble researchers with a diverse range of expertise but focused on the same set of ecological challenges. The recently established Great Lakes User Group (GLUG) is NEON’s first domain specific ensemble of researchers, whose goal is to address scientific and technical issues specific to the Great Lakes Domain 5 (D05) by using NEON data to enable advancement of ecosystem science. Here, we report on GLUG’s kick off workshop, which comprised lightning talks, keynote presentations, breakout brainstorming sessions and field site visits. Together, these activities created an environment to foster and strengthen GLUG and NEON user engagement. The tangible outcomes of the workshop exceeded initial expectations and include plans for (i) two journal articles (in addition to this one), (ii) two potential funding proposals, (iii) an assignable assets request and (iv) development of classroom activities using NEON datasets. The success of this 2.5-day event was due to a combination of factors, including establishment of clear objectives, adopting engaging activities and providing opportunities for active participation and inclusive collaboration with diverse participants. Given the success of this approach we encourage others, wanting to organize similar groups of researchers, to adopt the workshop framework presented here which will strengthen existing collaborations and foster new ones, together with raising greater awareness and promotion of use of NEON datasets. Establishing domain specific user groups will help bridge the scale gap between site level data collection and addressing regional and larger ecological challenges. 

Abstract Nitrogen (N) is a key limiting nutrient in terrestrial ecosystems, but there remain critical gaps in our ability to predict and model controls on soil N cycling. This may be in part due to lack of standardized sampling across broad spatial–temporal scales. Here, we introduce a continentally distributed, publicly available data set collected by the National Ecological Observatory Network (NEON) that can help fill these gaps. First, we detail the sampling design and methods used to collect and analyze soil inorganic N pool and net flux rate data from 47 terrestrial sites. We address methodological challenges in generating a standardized data set, even for a network using uniform protocols. Then, we evaluate sources of variation within the sampling design and compare measured net N mineralization to simulated fluxes from the Community Earth System Model 2 (CESM2). We observed wide spatiotemporal variation in inorganic N pool sizes and net transformation rates. Site explained the most variation in NEON’s stratified sampling design, followed by plots within sites. Organic horizons had larger pools and net N transformation rates than mineral horizons on a sample weight basis. The majority of sites showed some degree of seasonality in N dynamics, but overall these temporal patterns were not matched by CESM2, leading to poor correspondence between observed and modeled data. Looking forward, these data can reveal new insights into controls on soil N cycling, especially in the context of other environmental data sets provided by NEON, and should be leveraged to improve predictive modeling of the soil N cycle. 

Abstract Assessment of socio-environmental problems and the search for solutions often require intersecting geospatial data on environmental factors and human population densities. In the United States, Census data is the most common source for information on population. However, timely acquisition of such data at sufficient spatial resolution can be problematic, especially in cases where the analysis area spans urban-rural gradients. With this data release, we provide a 30-m resolution population estimate for the contiguous United States. The workflow dasymetrically distributes Census block level population estimates across all non-transportation impervious surfaces within each Census block. The methodology is updatable using the most recent Census data and remote sensing-based observations of impervious surface area. The dataset, known as the U.G.L.I (updatable gridded lightweight impervious) population dataset, compares favorably against other population data sources, and provides a useful balance between resolution and complexity. 

Abstract The National Ecological Observatory Network Terrestrial Observation System (NEON TOS) produces open‐access data products that allow data users to investigate the impact of change drivers on key “sentinel” taxa and soils. The spatial and temporal sampling strategy that coordinates implementation of these protocols enables integration across TOS products and with products generated by NEON aquatic, remote sensing, and terrestrial instrument subsystems. Here, we illustrate the plots and sampling units that make up the physical foundation of a NEON TOS site, and we describe the scales (subplot, plot, airshed, and site) at which sampling is spatially colocated across protocols and subsystems. We also describe how moderate resolution imaging spectroradiometer‐enhanced vegetation index (MODIS‐EVI) phenology data are used to temporally coordinate TOS sampling within and across years at the continental scale of the observatory. Individually, TOS protocols produce data products that provide insight into populations, communities, and ecosystem processes. Within the spatial and temporal framework that guides cross‐protocol implementation, the ability to draw inference across data products is enhanced. To illustrate this point, we develop an example using R software that links two TOS data products collected with different temporal frequencies at both plot and site spatial scales. A thorough understanding of how TOS protocols are integrated with each other in space and time, and with other NEON subsystems, is necessary to leverage NEON data products to maximum effect. For example, a researcher must understand the spatial and temporal scales at which soil biogeochemistry data, soil microbe biomass data, and plant litter production and chemistry data may be combined to quantify soil nutrient stocks and fluxes across NEON sites. We present clear links among TOS protocols and across NEON subsystems that will enhance the utility of NEON TOS data products for the data user community. 

Abstract Invasive species science has focused heavily on the invasive agent. However, management to protect native species also requires a proactive approach focused on resident communities and the features affecting their vulnerability to invasion impacts. Vulnerability is likely the result of factors acting across spatial scales, from local to regional, and it is the combined effects of these factors that will determine the magnitude of vulnerability. Here, we introduce an analytical framework that quantifies the scale‐dependent impact of biological invasions on native richness from the shape of the native species–area relationship (SAR). We leveraged newly available, biogeographically extensive vegetation data from the U.S. National Ecological Observatory Network to assess plant community vulnerability to invasion impact as a function of factors acting across scales. We analyzed more than 1000 SARs widely distributed across the USA along environmental gradients and under different levels of non‐native plant cover. Decreases in native richness were consistently associated with non‐native species cover, but native richness was compromised only at relatively high levels of non‐native cover. After accounting for variation in baseline ecosystem diversity, net primary productivity, and human modification, ecoregions that were colder and wetter were most vulnerable to losses of native plant species at the local level, while warmer and wetter areas were most susceptible at the landscape level. We also document how the combined effects of cross‐scale factors result in a heterogeneous spatial pattern of vulnerability. This pattern could not be predicted by analyses at any single scale, underscoring the importance of accounting for factors acting across scales. Simultaneously assessing differences in vulnerability between distinct plant communities at local, landscape, and regional scales provided outputs that can be used to inform policy and management aimed at reducing vulnerability to the impact of plant invasions. 

Abstract Streams and rivers are major sources of greenhouse gases (GHGs) to the atmosphere, as carbon and nitrogen are converted and outgassed during transport. Although our understanding of drivers of individual GHG fluxes has improved with numerous site‐specific studies and global‐scale compilations, our ability to parse out interrelated physical and biogeochemical drivers of gas concentrations is limited by a lack of consistently collected, temporally continuous samples of GHGs and their associated drivers. We present a first analysis of such a dataset collected by the National Ecological Observatory Network across 27 streams and rivers across ecoclimatic domains of the United States. Average concentrations of CO₂ranged from 36.9 ± 0.88 to 404 ± 33 μmol L⁻¹, CH₄from 0.003 ± 0.0003 to 4.99 ± 0.72 μmol L⁻¹, and N₂O from 0.015 to 0.04 μmol L⁻¹and spanned ranges of previous global compilations. Both CO₂and CH₄were strongly affected by physical drivers including mean air temperature and stream slope, as well as by dissolved oxygen and total nitrogen concentrations. N₂O was exclusively correlated with total nitrogen concentrations. Results suggested that potential for gas exchange dominated patterns in gas concentrations at the site level, but contributions of in‐stream aerobic and anaerobic metabolism, and groundwater also likely varied across sites. The highest gas concentrations as well as highest variability occurred in low‐gradient, warmer, and nonperennial systems. These results are a first step in providing unprecedented, continuous estimates of GHG flux constrained by temporally variable physical and biogeochemical drivers of GHG production. 

Abstract Small waterbodies have potentially high greenhouse gas emissions relative to their small footprint on the landscape, although there is high uncertainty in model estimates. Scaling their carbon dioxide (CO₂) and methane (CH₄) exchange with the atmosphere remains challenging due to an incomplete understanding and characterization of spatial and temporal variability in CO₂and CH₄. Here, we measured partial pressures of CO₂(pCO₂) and CH₄(pCH₄) across 30 ponds and shallow lakes during summer in temperate regions of Europe and North America. We sampled each waterbody in three locations at three times during the growing season, and tested which physical, chemical, and biological characteristics related to the means and variability ofpCO₂andpCH₄in space and time. Summer means ofpCO₂andpCH₄were inversely related to waterbody size and positively related to floating vegetative cover;pCO₂was also positively related to dissolved phosphorus. Temporal variability in partial pressure in both gases weas greater than spatial variability. Although sampling on a single date was likely to misestimate mean seasonalpCO₂by up to 26%, mean seasonalpCH₄could be misestimated by up to 64.5%. Shallower systems displayed the most temporal variability inpCH₄and waterbodies with more vegetation cover had lower temporal variability. Inland waters remain one of the most uncertain components of the global carbon budget; understanding spatial and temporal variability will ultimately help us to constrain our estimates and inform research priorities. 

Abstract Ponds are often identified by their small size and shallow depths, but the lack of a universal evidence-based definition hampers science and weakens legal protection. Here, we compile existing pond definitions, compare ecosystem metrics (e.g., metabolism, nutrient concentrations, and gas fluxes) among ponds, wetlands, and lakes, and propose an evidence-based pond definition. Compiled definitions often mentioned surface area and depth, but were largely qualitative and variable. Government legislation rarely defined ponds, despite commonly using the term. Ponds, as defined in published studies, varied in origin and hydroperiod and were often distinct from lakes and wetlands in water chemistry. We also compared how ecosystem metrics related to three variables often seen in waterbody definitions: waterbody size, maximum depth, and emergent vegetation cover. Most ecosystem metrics (e.g., water chemistry, gas fluxes, and metabolism) exhibited nonlinear relationships with these variables, with average threshold changes at 3.7 ± 1.8 ha (median: 1.5 ha) in surface area, 5.8 ± 2.5 m (median: 5.2 m) in depth, and 13.4 ± 6.3% (median: 8.2%) emergent vegetation cover. We use this evidence and prior definitions to define ponds as waterbodies that are small (< 5 ha), shallow (< 5 m), with < 30% emergent vegetation and we highlight areas for further study near these boundaries. This definition will inform the science, policy, and management of globally abundant and ecologically significant pond ecosystems. 

Abstract Livestock agriculture accounts for ∼15% of global anthropogenic greenhouse gas (GHG) emissions. Recently, natural climate solutions (NCS) have been identified to mitigate farm‐scale GHG emissions. Nevertheless, their impacts are difficult to quantify due to farm spatial heterogeneity and effort required to measure changes in carbon stocks. Remote sensing (RS) models are difficult to parameterize for heterogeneous agricultural landscapes. Eddy covariance (EC) in combination with novel techniques that quantitatively match source area variations could help update such vegetation‐specific parameters while accounting for pronounced heterogeneity. We evaluate a plant physiological parameter, the maximum quantum yield (MQY), which is commonly used to calculate gross and net primary productivity in RS applications. RS models often rely on spatially invariable MQY, which leads to inconsistencies between RS and EC models. We evaluate if EC data improve RS models by updating crop specific MQYs to quantify agricultural GHG mitigation potentials. We assessed how farm harvest compared to annual sums of (a) RS without improvements, (b) EC results, and (c) EC‐RS models. We then estimated emissions to calculate the annual GHG balance, including mitigation through plant carbon uptake. Our results indicate that EC‐RS models significantly improved the prediction of crop yields. The EC model captures diurnal variations in carbon dynamics in contrast to RS models based on input limitations. A net zero GHG balance indicated that perennial vegetation mitigated over 60% of emissions while comprising 40% of the landscape. We conclude that the combination of RS and EC can improve the quantification of NCS in agroecosystems.