Our meta-analysis of 126 nitrogen addition experiments evaluated nitrogen (N) limitation of net primary production (NPP) in terrestrial ecosystems. We tested the hypothesis that N limitation is widespread among biomes and influenced by geography and climate. We used the response ratio (R approximately equal ANPP(N)/ANPP(ctrl)) of aboveground plant growth in fertilized to control plots and found that most ecosystems are nitrogen limited with an average 29% growth response to nitrogen (i.e., R = 1.29). The response ratio was significant within temperate forests (R = 1.19), tropical forests (R = 1.60), temperate grasslands (R = 1.53), tropical grasslands (R = 1.26), wetlands (R = 1.16), and tundra (R = 1.35), but not deserts. Eight tropical forest studies had been conducted on very young volcanic soils in Hawaii, and this subgroup was strongly N limited (R = 2.13), which resulted in a negative correlation between forest R and latitude. The degree of N limitation in the remainder of the tropical forest studies (R = 1.20) was comparable to that of temperate forests, and when the young Hawaiian subgroup was excluded, forest R did not vary with latitude. Grassland response increased with latitude, but was independent of temperature and precipitation. These results suggest that the global N and C cycles interact strongly and that geography can mediate ecosystem response to N within certain biome types.

Ecological models help us understand how ecosystems function, predict responses to global change, and identify future research needs. However, widespread use of models is limited by the technical challenges of model–data synthesis and information management. To address these challenges, we present an ecoinformatic workflow, the Predictive Ecosystem Analyzer (PEcAn), which facilitates model analysis. Herein we describe the PEcAn modules that synthesize plant trait data to estimate model parameters, propagate parameter uncertainties through to model output, and evaluate the contribution of each parameter to model uncertainty. We illustrate a comprehensive approach to the estimation of parameter values, starting with a statement of prior knowledge that is refined by species‐level data using Bayesian meta‐analysis; this is the first use of a rigorous meta‐analysis to inform the parameters of a mechanistic ecosystem model. Parameter uncertainty is propagated using ensemble methods to estimate model uncertainty. Variance decomposition allows us to quantify the contribution of each parameter to model uncertainty; this information can be used to prioritize subsequent data collection. By streamlining the use of models and focusing efforts to identify and constrain the dominant sources of uncertainty in model output, the approach used by PEcAn can speed scientific progress. We demonstrate PEcAn's ability to incorporate data to reduce uncertainty in productivity of a perennial grass monoculture (Panicum virgatum L.) modeled by the Ecosystem Demography model. Prior estimates were specified for 15 model parameters, and species‐level data were available for seven of these. Meta‐analysis of species‐level data substantially reduced the contribution of three parameters (specific leaf area, maximum carboxylation rate, and stomatal slope) to overall model uncertainty. By contrast, root turnover rate, root respiration rate, and leaf width had little effect on model output; therefore trait data had little impact on model uncertainty. For fine‐root allocation, the decrease in parameter uncertainty was offset by an increase in model sensitivity. Remaining model uncertainty is driven by growth respiration, fine‐root allocation, leaf turnover rater, and specific leaf area. By establishing robust channels of feedback between data collection and ecosystem modeling, PEcAn provides a framework for more efficient and integrative science.

Ammonium oxidation by autotrophic ammonia-oxidizing bacteria (AOB) is a key process in agricultural and natural ecosystems and has a large global impact. In the past, the ecology and physiology of AOB were not well understood because these organisms are notoriously difficult to culture. Recent applications of molecular techniques have advanced our knowledge of AOB, but the necessity of using PCR-based techniques has made quantitative measurements difficult. A quantitative real-time PCR assay targeting part of the ammoniamonooxygenase gene (amoA) was developed to estimate AOB population size in soil. This assay has a detection limit of 1.3 ؋ 10 5 cells/g of dry soil. The effect of the ammonium concentration on AOB population density was measured in soil microcosms by applying 0, 1.5, or 7.5 mM ammonium sulfate. AOB population size and ammonium and nitrate concentrations were monitored for 28 days after (NH 4 ) 2 SO 4 application. AOB populations in amended treatments increased from an initial density of approximately 4 ؋ 10 6 cells/g of dry soil to peak values (day 7) of 35 ؋ 10 6 and 66 ؋ 10 6 cells/g of dry soil in the 1.5 and 7.5 mM treatments, respectively. The population size of total bacteria (quantified by real-time PCR with a universal bacterial probe) remained between 0.7 ؋ 10 9 and 2.2 ؋ 10 9 cells/g of soil, regardless of the ammonia concentration. A fertilization experiment was conducted in a tomato field plot to test whether the changes in AOB density observed in microcosms could also be detected in the field. AOB population size increased from 8.9 ؋ 10 6 to 38.0 ؋ 10 6 cells/g of soil by day 39. Generation times were 28 and 52 h in the 1.5 and 7.5 mM treatments, respectively, in the microcosm experiment and 373 h in the ammonium treatment in the field study. Estimated oxidation rates per cell ranged initially from 0.5 to 25.0 fmol of NH 4 ؉ h ؊1 cell ؊1 and decreased with time in both microcosms and the field. Growth yields were 5.6 ؋ 10 6 , 17.5 ؋ 10 6 , and 1.7 ؋ 10 6 cells/mol of NH 4 ؉ in the 1.5 and 7.5 mM microcosm treatments and the field study, respectively. In a second field experiment, AOB population size was significantly greater in annually fertilized versus unfertilized soil, even though the last ammonium application occurred 8 months prior to measurement, suggesting a long-term effect of ammonium fertilization on AOB population size.Ammonium oxidation by autotrophic ammonia-oxidizing bacteria (AOB) is a key process in agricultural and natural ecosystems, with a large global impact. The product of this process, nitrite, is immediately oxidized by nitrite-oxidizing bacteria to nitrate, a nitrogen form susceptible to leaching. Nitrogen leaching can lead to groundwater pollution and surface and groundwater eutrophication. Nitrous oxide and nitric oxide, by-products of ammonia oxidation, contribute to the greenhouse effect and ozone layer depletion. On a local scale, loss of nitrate to groundwater and nitrous oxide and nitric oxide to the atmosphere reduces the amount of nitrogen available ...

Terrestrial biosphere models are designed to synthesize our current understanding of how ecosystems function, test competing hypotheses of ecosystem function against observations, and predict responses to novel conditions such as those expected under climate change. Reducing uncertainties in such models can improve both basic scientific understanding and our predictive capacity, but rarely are ecosystem models employed in the design of field campaigns. We provide a synthesis of carbon cycle uncertainty analyses conducted using the Predictive Ecosystem Analyzer ecoinformatics workflow with the Ecosystem Demography model v2. This work is a synthesis of multiple projects, using Bayesian data assimilation techniques to incorporate field data and trait databases across temperate forests, grasslands, agriculture, short rotation forestry, boreal forests, and tundra. We report on a number of data needs that span a wide array of diverse biomes, such as the need for better constraint on growth respiration, mortality, stomatal conductance, and water uptake. We also identify data needs that are biome specific, such as photosynthetic quantum efficiency at high latitudes. We recommend that future data collection efforts balance the bias of past measurements toward aboveground processes in temperate biomes with the sensitivities of different processes as represented by ecosystem models.