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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 Lignin is an abundant and complex plant polymer that may limit litter decomposition, yet lignin is sometimes a minor constituent of soil organic carbon (SOC). Accounting for diversity in soil characteristics might reconcile this apparent contradiction. Tracking decomposition of a lignin/litter mixture and SOC across different North American mineral soils using lab and field incubations, here we show that cumulative lignin decomposition varies 18-fold among soils and is strongly correlated with bulk litter decomposition, but not SOC decomposition. Climate legacy predicts decomposition in the lab, and impacts of nitrogen availability are minor compared with geochemical and microbial properties. Lignin decomposition increases with some metals and fungal taxa, whereas SOC decomposition decreases with metals and is weakly related with fungi. Decoupling of lignin and SOC decomposition and their contrasting biogeochemical drivers indicate that lignin is not necessarily a bottleneck for SOC decomposition and can explain variable contributions of lignin to SOC among ecosystems. 

In order to learn about broad scale ecological patterns, data from large-scale surveys must allow us to either estimate the correlations between the environment and an outcome and/or accurately predict ecological patterns. An important part of data collection is the sampling effort used to collect observations, which we decompose into two quantities: the number of observations or plots ( n ) and the per-observation/plot effort ( E ; e.g., area per plot). If we want to understand the relationships between predictors and a response variable, then lower model parameter uncertainty is desirable. If the goal is to predict a response variable, then lower prediction error is preferable. We aim to learn if and when aggregating data can help attain these goals. We find that a small sample size coupled with large observation effort coupled (few large) can yield better predictions when compared to a large number of observations with low observation effort (many small). We also show that the combination of the two values ( n and E ), rather than one alone, has an impact on parameter uncertainty. In an application to Forest Inventory and Analysis (FIA) data, we model the tree density of selected species at various amounts of aggregation using linear regression in order to compare the findings from simulated data to real data. The application supports the theoretical findings that increasing observational effort through aggregation can lead to improved predictions, conditional on the thoughtful aggregation of the observational plots. In particular, aggregations over extremely large and variable covariate space may lead to poor prediction and high parameter uncertainty. Analyses of large-range data can improve with aggregation, with implications for both model evaluation and sampling design: testing model prediction accuracy without an underlying knowledge of the datasets and the scale at which predictor variables operate can obscure meaningful results.