Search

Nutrient limitation is widespread in terrestrial ecosystems. Accordingly, representations of nitrogen (N) limitation in land models typically dampen rates of terrestrial carbon (C) accrual, compared with C‐only simulations. These previous findings, however, rely on soil biogeochemical models that implicitly represent microbial activity and physiology. Here we present results from a biogeochemical model testbed that allows us to investigate how an explicit versus implicit representation of soil microbial activity, as represented in the MIcrobial‐MIneral Carbon Stabilization (MIMICS) and Carnegie‐Ames‐Stanford Approach (CASA) soil biogeochemical models, respectively, influence plant productivity, and terrestrial C and N fluxes at initialization and over the historical period. When forced with common boundary conditions, larger soil C pools simulated by the MIMICS model reflect longer inferred soil organic matter (SOM) turnover times than those simulated by CASA. At steady state, terrestrial ecosystems experience greater N limitation when using the MIMICS‐CN model, which also increases the inferred SOM turnover time. Over the historical period, however, warming‐induced acceleration of SOM decomposition over high latitude ecosystems increases rates of N mineralization in MIMICS‐CN. This reduces N limitation and results in faster rates of vegetation C accrual. Moreover, as SOM stoichiometry is an emergent property of MIMICS‐CN, we highlight opportunities to deepen understanding of sources of persistent SOM and explore its potential sensitivity to environmental change. Our findings underscore the need to improve understanding and representation of plant and microbial resource allocation and competition in land models that represent coupled biogeochemical cycles under global change scenarios. Plain Language Summary: Nitrogen limitation of terrestrial ecosystems is common, and crates feedbacks between aboveground and belowground biogeochemical cycles. We present a novel analysis looking at how the explicit versus implicit representation of soil microbial activity influences ecosystem carbon and nitrogen fluxes in a global biogeochemical model. With the microbial explicit model, MIcrobial‐MIneral Carbon Stabilization‐carbon‐nitrogen, we found increases in the inferred turnover time of soil organic matter (SOM) that were caused by plant‐soil feedbacks from nitrogen limitation of plant productivity. Over the historical period, we found that warming‐induced acceleration of SOM decomposition resulted in higher rates of nitrogen mineralization and vegetation biomass accrual. Collectively, these findings present new opportunities to investigate plant‐soil interactions with a model that explicitly represents microbial decomposers. Key Points: We present a global scale soil carbon and nitrogen biogeochemical model that explicitly represents soil microbial activity, MIcrobial‐MIneral Carbon Stabilization‐carbon‐nitrogen (MIMICS‐CN)Nitrogen limitation of plant productivity increases soil organic matter turnover time with MIMICS‐CNSoil texture and litter quality affect prognostic soil carbon to nitrogen ratios that are simulated with MIMICS‐CN [ABSTRACT FROM AUTHOR]

Terrestrial ecosystems regulate Earth's climate through water, energy, and biogeochemical transformations. Despite a key role in regulating the Earth system, terrestrial ecology has historically been underrepresented in the Earth system models (ESMs) that are used to understand and project global environmental change. Ecology and Earth system modeling must be integrated for scientists to fully comprehend the role of ecological systems in driving and responding to global change. Ecological insights can improve ESM realism and reduce process uncertainty, while ESMs offer ecologists an opportunity to broadly test ecological theory and increase the impact of their work by scaling concepts through time and space. Despite this mutualism, meaningfully integrating the two remains a persistent challenge, in part because of logistical obstacles in translating processes into mathematical formulas and identifying ways to integrate new theories and code into large, complex model structures. To help overcome this interdisciplinary challenge, we present a framework consisting of a series of interconnected stages for integrating a new ecological process or insight into an ESM. First, we highlight the multiple ways that ecological observations and modeling iteratively strengthen one another, dispelling the illusion that the ecologist's role ends with initial provision of data. Second, we show that many valuable insights, products, and theoretical developments are produced through sustained interdisciplinary collaborations between empiricists and modelers, regardless of eventual inclusion of a process in an ESM. Finally, we provide concrete actions and resources to facilitate learning and collaboration at every stage of data‐model integration. This framework will create synergies that will transform our understanding of ecology within the Earth system, ultimately improving our understanding of global environmental change, and broadening the impact of ecological research. [ABSTRACT FROM AUTHOR]

Data collected from research networks present opportunities to test theories and develop models about factors responsible for the long-term persistence and vulnerability of soil organic matter (SOM). Synthesizing datasets collected by different research networks presents opportunities to expand the ecological gradients and scientific breadth of information available for inquiry. Synthesizing these data, are challenging, especially considering the legacy of soils data that has already been collected and an expansion of new network science initiatives. To facilitate this effort, here we present the SOils DAta Harmonization database (SoDaH; https://lter.github.io/som-website, last accessed 15 July 2020), a flexible database designed to harmonize diverse SOM datasets from multiple research networks. SoDaH is built on several network science efforts in the United States, but the tools built for SoDaH aim to provide an open-access resource to facilitate and automate further harmonization and synthesis of soil carbon data. Moreover, SoDaH allows for individual locations to contribute results from experimental manipulations, repeated measurements from long-term studies, and local- to regional-scale gradients across ecosystems or landscapes. Finally, we also provide data visualization and analysis tools that can be used to query and analyze the aggregated database. The SoDaH v1.0 dataset is archived and available at https://doi.org/10.6073/pasta/9733f6b6d2ffd12bf126dc36a763e0b4 (Wieder et al., 2020). [ABSTRACT FROM AUTHOR]

Explicit consideration of microbial physiology in soil biogeochemical models that represent coupled carbon-nitrogen dynamics presents opportunities to deepen understanding of ecosystem responses to environmental change. The MIcrobial-MIneral Carbon Stabilization (MIMICS) model explicitly represents microbial physiology and physicochemical stabilization of soil carbon (C) on regional and global scales. Here we present a new version of MIMICS with coupled C and nitrogen (N) cycling through litter, microbial, and soil organic matter (SOM) pools. The model was parameterized and validated against C and N data from the Long-Term Inter-site Decomposition Experiment Team (LIDET; 6 litter types, 10 years of observations, 13 sites across North America). The model simulates C and N losses from litterbags in the LIDET study with reasonable accuracy (C: R2 = 0.63, N: R2 = 0.29) results that are comparable with simulations from the DAYCENT model that implicitly represents microbial activity (C: R2 = 0.67, N: R2 = 0.30). Subsequently, we evaluated equilibrium values of stocks (total soil C and N, microbial biomass C and N, inorganic N) and microbial process rates (soil heterotrophic respiration, N mineralization) simulated by MIMICS-CN across the 13 simulated LIDET sites against published observations from other continent-wide datasets. We found that MIMICS-CN produces equilibrium values in line with measured values, showing that the model generates plausible estimates of ecosystem soil biogeochemical dynamics across continental-scale gradients. MIMICS-CN provides a platform for coupling C and N projections in a microbial-explicit model but experiments still need to identify the physiological and stoichiometric characteristics of soil microbes, especially under environmental change scenarios. [ABSTRACT FROM AUTHOR]

We review existing challenges and possible solutions for increasing small-scale sustainable beef production in New England. Beef production policies are most effectively implemented when the impacts of regulation, facility accessibility, and seasonality are considered. Limitations surrounding scheduling availability, skill, and services offered create challenges that reduce processing facility accessibility for small-scale producers. Relationship building facilitates trust and communication between all members of the beef value chain. Voluntary certifications offer value translation and financial networks for beef producers. State inspection and slaughter/processing programs provide scale-appropriate policy solutions for sustainable beef in New England. [ABSTRACT FROM AUTHOR]