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  1. Key Points The NEON sites were estimated to have large soil organic carbon (SOC) loss in both topsoil and subsoil during 1984–2014 The carbon sequestration potential is limited in well‐developed and near carbon‐saturated soils in managed ecosystems Runoff/erosion and leaching, vertical translocation, and mineral sorption are dominant factors affecting SOC variation at National Ecological Observatory Network sites 
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    Free, publicly-accessible full text available May 1, 2024
  2. Data Description:

    To improve SOC estimation in the United States, we upscaled site-based SOC measurements to the continental scale using multivariate geographic clustering (MGC) approach coupled with machine learning models. First, we used the MGC approach to segment the United States at 30 arc second resolution based on principal component information from environmental covariates (gNATSGO soil properties, WorldClim bioclimatic variables, MODIS biological variables, and physiographic variables) to 20 SOC regions. We then trained separate random forest model ensembles for each of the SOC regions identified using environmental covariates and soil profile measurements from the International Soil Carbon Network (ISCN) and an Alaska soil profile data. We estimated United States SOC for 0-30 cm and 0-100 cm depths were 52.6 + 3.2 and 108.3 + 8.2 Pg C, respectively.

    Files in collection (32):

    Collection contains 22 soil properties geospatial rasters, 4 soil SOC geospatial rasters, 2 ISCN site SOC observations csv files, and 4 R scripts

    gNATSGO TIF files:

    ├── available_water_storage_30arc_30cm_us.tif                   [30 cm depth soil available water storage]
    ├── available_water_storage_30arc_100cm_us.tif                 [100 cm depth soil available water storage]
    ├── caco3_30arc_30cm_us.tif                                                 [30 cm depth soil CaCO3 content]
    ├── caco3_30arc_100cm_us.tif                                               [100 cm depth soil CaCO3 content]
    ├── cec_30arc_30cm_us.tif                                                     [30 cm depth soil cation exchange capacity]
    ├── cec_30arc_100cm_us.tif                                                   [100 cm depth soil cation exchange capacity]
    ├── clay_30arc_30cm_us.tif                                                     [30 cm depth soil clay content]
    ├── clay_30arc_100cm_us.tif                                                   [100 cm depth soil clay content]
    ├── depthWT_30arc_us.tif                                                        [depth to water table]
    ├── kfactor_30arc_30cm_us.tif                                                 [30 cm depth soil erosion factor]
    ├── kfactor_30arc_100cm_us.tif                                               [100 cm depth soil erosion factor]
    ├── ph_30arc_100cm_us.tif                                                      [100 cm depth soil pH]
    ├── ph_30arc_100cm_us.tif                                                      [30 cm depth soil pH]
    ├── pondingFre_30arc_us.tif                                                     [ponding frequency]
    ├── sand_30arc_30cm_us.tif                                                    [30 cm depth soil sand content]
    ├── sand_30arc_100cm_us.tif                                                  [100 cm depth soil sand content]
    ├── silt_30arc_30cm_us.tif                                                        [30 cm depth soil silt content]
    ├── silt_30arc_100cm_us.tif                                                      [100 cm depth soil silt content]
    ├── water_content_30arc_30cm_us.tif                                      [30 cm depth soil water content]
    └── water_content_30arc_100cm_us.tif                                   [100 cm depth soil water content]

    SOC TIF files:

    ├──30cm SOC mean.tif                             [30 cm depth soil SOC]
    ├──100cm SOC mean.tif                           [100 cm depth soil SOC]
    ├──30cm SOC CV.tif                                 [30 cm depth soil SOC coefficient of variation]
    └──100cm SOC CV.tif                              [100 cm depth soil SOC coefficient of variation]

    site observations csv files:

    ISCN_rmNRCS_addNCSS_30cm.csv       30cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data

    ISCN_rmNRCS_addNCSS_100cm.csv       100cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data

    Data format:

    Geospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution.

    Geospatial projection

    GEOGCS["GCS_WGS_1984", DATUM["D_WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["Degree",0.017453292519943295]] (base) [jbk@theseus ltar_regionalization]$ g.proj -w GEOGCS["wgs84", DATUM["WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]]


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  3. Soil nitrous oxide (N 2 O) emissions are an important driver of climate change and are a major mechanism of labile nitrogen (N) loss from terrestrial ecosystems. Evidence increasingly suggests that locations on the landscape that experience biogeochemical fluxes disproportionate to the surrounding matrix (hot spots) and time periods that show disproportionately high fluxes relative to the background (hot moments) strongly influence landscape-scale soil N 2 O emissions. However, substantial uncertainties remain regarding how to measure and model where and when these extreme soil N 2 O fluxes occur. High-frequency datasets of soil N 2 O fluxes are newly possible due to advancements in field-ready instrumentation that uses cavity ring-down spectroscopy (CRDS). Here, we outline the opportunities and challenges that are provided by the deployment of this field-based instrumentation and the collection of high-frequency soil N 2 O flux datasets. While there are substantial challenges associated with automated CRDS systems, there are also opportunities to utilize these near-continuous data to constrain our understanding of dynamics of the terrestrial N cycle across space and time. Finally, we propose future research directions exploring the influence of hot moments of N 2 O emissions on the N cycle, particularly considering the gaps surrounding how global change forces are likely to alter N dynamics in the future. 
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  4. 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.

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  5. null (Ed.)
  6. Abstract. Tropical ecosystems contribute significantly to global emissionsof methane (CH4), and landscape topography influences the rate ofCH4 emissions from wet tropical forest soils. However, extreme eventssuch as drought can alter normal topographic patterns of emissions. Here weexplain the dynamics of CH4 emissions during normal and droughtconditions across a catena in the Luquillo Experimental Forest, Puerto Rico.Valley soils served as the major source of CH4 emissions in a normalprecipitation year (2016), but drought recovery in 2015 resulted in dramaticpulses in CH4 emissions from all topographic positions. Geochemicalparameters including (i) dissolved organic carbon (C), acetate, and soil pH and (ii) hydrological parameters like soil moisture and oxygen (O2)concentrations varied across the catena. During the drought, soil moisturedecreased in the slope and ridge, and O2 concentrations increased in thevalley. We simulated the dynamics of CH4 emissions with theMicrobial Model for Methane Dynamics-Dual Arrhenius and Michaelis–Menten (M3D-DAMM), which couples a microbialfunctional group CH4 model with a diffusivity module for solute and gastransport within soil microsites. Contrasting patterns of soil moisture,O2, acetate, and associated changes in soil pH with topographyregulated simulated CH4 emissions, but emissions were also altered byrate-limited diffusion in soil microsites. Changes in simulated availablesubstrate for CH4 production (acetate, CO2, and H2) andoxidation (O2 and CH4) increased the predicted biomass ofmethanotrophs during the drought event and methanogens during droughtrecovery, which in turn affected net emissions of CH4. A variance-basedsensitivity analysis suggested that parameters related to aceticlasticmethanogenesis and methanotrophy were most critical to simulate net CH4emissions. This study enhanced the predictive capability for CH4emissions associated with complex topography and drought in wet tropicalforest soils. 
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  7. null (Ed.)
    Disturbances fundamentally alter ecosystem functions, yet predicting their impacts remains a key scientific challenge. While the study of disturbances is ubiquitous across many ecological disciplines, there is no agreed-upon, cross-disciplinary foundation for discussing or quantifying the complexity of disturbances, and no consistent terminology or methodologies exist. This inconsistency presents an increasingly urgent challenge due to accelerating global change and the threat of interacting disturbances that can destabilize ecosystem responses. By harvesting the expertise of an interdisciplinary cohort of contributors spanning 42 institutions across 15 countries, we identified an essential limitation in disturbance ecology: the word ‘disturbance’ is used interchangeably to refer to both the events that cause, and the consequences of, ecological change, despite fundamental distinctions between the two meanings. In response, we developed a generalizable framework of ecosystem disturbances, providing a well-defined lexicon for understanding disturbances across perspectives and scales. The framework results from ideas that resonate across multiple scientific disciplines and provides a baseline standard to compare disturbances across fields. This framework can be supplemented by discipline-specific variables to provide maximum benefit to both inter- and intra-disciplinary research. To support future syntheses and meta-analyses of disturbance research, we also encourage researchers to be explicit in how they define disturbance drivers and impacts, and we recommend minimum reporting standards that are applicable regardless of scale. Finally, we discuss the primary factors we considered when developing a baseline framework and propose four future directions to advance our interdisciplinary understanding of disturbances and their social-ecological impacts: integrating across ecological scales, understanding disturbance interactions, establishing baselines and trajectories, and developing process-based models and ecological forecasting initiatives. Our experience through this process motivates us to encourage the wider scientific community to continue to explore new approaches for leveraging Open Science principles in generating creative and multidisciplinary ideas. 
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