Humans dominate many important Earth system processes including the nitrogen (N) cycle. Atmospheric N deposition affects fundamental processes such as carbon cycling, climate regulation, and biodiversity, and could result in changes to fundamental Earth system processes such as primary production. Both modelling and experimentation have suggested a role for anthropogenically altered N deposition in increasing productivity, nevertheless, current understanding of the relative strength of N deposition with respect to other controls on production such as edaphic conditions and climate is limited. Here we use an international multiscale data set to show that atmospheric N deposition is positively correlated to aboveground net primary production (ANPP) observed at the 1-m 2 level across a wide range of herbaceous ecosystems. N deposition was a better predictor than climatic drivers and local soil conditions, explaining 16% of observed variation in ANPP globally with an increase of 1 kg NÁha À1 Áyr À1 increasing ANPP by 3%. Soil pH explained 8% of observed variation in ANPP while climatic drivers showed no significant relationship. Our results illustrate that the incorporation of global N deposition patterns in Earth system models are likely to substantially improve estimates of primary production in herbaceous systems. In herbaceous systems across the world, humans appear to be partially driving local ANPP through impacts on the N cycle.

Leaf traits are frequently measured in ecology to provide a 'common currency' for predicting how anthropogenic pressures impact ecosystem function. Here, we test whether leaf traits consistently respond to experimental treatments across globally distributed grassland sites across four continents. We find specific leaf area (SLA; leaf area per unit mass), a commonly measured morphological trait to

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The understorey light regime and its influence on forest tree seedling distribution was examined in isolated riparian forest patches where large numbers of co-existing species have been found. Understorey light intensity was highest at the forest edge, approaching 13% of full sunlight, but rapidly decreased towards the forest interior. By 7-12 m, light levels had stabilized at <2.0%, comparable to light levels in the interior of continuous tropical forest. Total seedling densities were not correlated with variations in understorey light intensity, suggesting that rates of germination and establishment were similar throughout the forest. However, examination of seedling light sensitivity among six common riparian tree species revealed differences in distribution: five species had higher seedling abundance in the darker forest interior or at the brighter forest-patch edge while one species was indifferent to light variation. These results suggest that the riparian forest light regime has sufficient variation to support several regeneration strategies, despite the small patch sizes.

Causal effects of biodiversity on ecosystem functions can be estimated using experimental or observational designs — designs that pose a tradeoff between drawing credible causal inferences from correlations and drawing generalizable inferences. Here, we develop a design that reduces this tradeoff and revisits the question of how plant species diversity affects productivity. Our design leverages longitudinal data from 43 grasslands in 11 countries and approaches borrowed from fields outside of ecology to draw causal inferences from observational data. Contrary to many prior studies, we estimate that increases in plot-level species richness caused productivity to decline: a 10% increase in richness decreased productivity by 2.4%, 95% CI [−4.1, −0.74]. This contradiction stems from two sources. First, prior observational studies incompletely control for confounding factors. Second, most experiments plant fewer rare and non-native species than exist in nature. Although increases in native, dominant species increased productivity, increases in rare and non-native species decreased productivity, making the average effect negative in our study. By reducing the tradeoff between experimental and observational designs, our study demonstrates how observational studies can complement prior ecological experiments and inform future ones.