National Ecological Observatory Network

Abstract. A multi-year analysis of aerosol optical depth (AOD, τ) and Ångström exponent (α) was conducted using ground-based photometer data from 15 Arctic and 11 Antarctic sites. Extending the dataset of (Tomasi et al., 2015) through December 2024, the study incorporates stellar and lunar photometric observations to fill data gaps during the polar night. Daily mean values of τ at 0.500 µm and α (0.440–0.870 µm) were used to derive monthly means and seasonal histograms. In the Arctic, persistent haze events in winter and early spring lead to peak τ values. A decreasing trend in Arctic τ suggests the impact of European emission regulations, while biomass-burning aerosols are becoming more significant. In Antarctica, τ increases from the plateau to the coast. Fine-mode aerosols dominate in summer-autumn, while coarse-mode particles are more prevalent in winter-spring. Shipborne photometer data align well with ground-based measurements, confirming the reliability of mobile observations. Trend analyses using the Mann-Kendall test and Theil-Sen regression indicate a significant negative trend in τ at Andenes (−2.43 % per year), likely driven by reduced anthropogenic emissions. Antarctic stations such as Syowa and South Pole show positive trends (+3.84 % and +3.54 % per year), though these are subject to uncertainties from data limitations and instrument changes. This work contributes to the Polar-AOD network (https://polaraod.net/, last access: 15 May 2025), enhancing the understanding of aerosol variability and long-term trends in polar regions while promoting open data access for the scientific community.

Abstract Accurate quantification of soil carbon fluxes is essential to reduce uncertainty in estimates of the terrestrial carbon sink. However, these fluxes vary over time and across ecosystem types and so, it can be difficult to estimate them accurately across large scales. The flux‐gradient method estimates soil carbon fluxes using co‐located measurements of soil CO 2 concentration, soil temperature, soil moisture and other soil properties. The National Ecological Observatory Network (NEON) provides such data across 20 ecoclimatic domains spanning the continental U.S., Puerto Rico, Alaska and Hawai‘i. We present an R software package ( neonSoilFlux ) that acquires soil environmental data to compute half‐hourly soil carbon fluxes for each soil replicate plot at a given terrestrial NEON site. To assess the computed fluxes, we visited six focal NEON sites and measured soil carbon fluxes using a closed‐dynamic chamber approach. Outputs from the neonSoilFlux showed agreement with measured fluxes ( R 2 between measured and neonSoilFlux outputs ranging from 0.12 to 0.77 depending on calculation method used); measured outputs generally fell within the range of calculated uncertainties from the gradient method. Calculated fluxes from neonSoilFlux aggregated to the daily scale exhibited expected site‐specific seasonal patterns. While the flux‐gradient method is broadly effective, its accuracy is highly sensitive to site‐specific inputs, including the extent to which gap‐filing techniques are used to interpolate missing sensor data and to estimates of soil diffusivity and moisture content. Future refinement and validation of neonSoilFlux outputs can contribute to existing databases of soil carbon flux measurements, providing near real‐time estimates of a critical component of the terrestrial carbon cycle.

<strong class="journal-contentHeaderColor">Abstract.</strong> A multi-year analysis of aerosol optical depth (AOD, &tau;) and Ångström exponent (&alpha;) was conducted using ground-based photometer data from 15 Arctic and 11 Antarctic sites. Extending the dataset of Tomasi et al. 2015 through December 2024, the study incorporates stellar and lunar photometric observations to fill data gaps during the polar night. Daily mean values of &tau; at 0.500 <em>&mu;</em>m and &alpha; (0.440&ndash;0.870 <em>&mu;</em>m) were used to derive monthly means and seasonal histograms. In the Arctic, persistent haze events in winter and early spring lead to peak &tau; values. A decreasing trend in Arctic &tau; suggests the impact of European emission regulations, while biomass-burning aerosols are becoming more significant. In Antarctica, &tau; increases from the plateau to the coast. Fine-mode aerosols dominate in summer-autumn, while coarse-mode particles are more prevalent in winter-spring. Shipborne photometer data align well with ground-based measurements, confirming the reliability of mobile observations. Trend analyses using the Mann-Kendall test and Theil-Sen regression indicate a significant negative trend in &tau; at Andenes (-2.43 % per year), likely driven by reduced anthropogenic emissions. Antarctic stations such as Syowa and South Pole show positive trends (+3.84 % and +3.54 % per year), though these are subject to uncertainties from data limitations and instrument changes. This work contributes to the Polar-AOD network (<a href="https://polaraod.net/" target="_blank" rel="noopener">https://polaraod.net/</a>; last access 15/05/2025), enhancing the understanding of aerosol variability and long-term trends in polar regions while promoting open data access for the scientific community.

ABSTRACTBelowground eukaryotic diversity serves a vital role in soil ecosystem functioning, yet the composition, structure, and macroecology of these communities are significantly under‐characterized. The National Ecological Observatory Network (NEON) provides publicly available datasets from long‐term surveillance of numerous taxa and ecosystem properties. However, this dataset is not routinely evaluated for its eukaryotic component, likely because analyzing metagenomes for eukaryotic sequences is hampered by low relative sequence abundance, large genomes, poorer eukaryote representation in public reference databases, and is not yet mainstream. We mined the NEON soil metagenome datasets for 18S rRNA sequences using a custom‐built pipeline and produced a preliminary assessment of biodiversity trends in North American soil eukaryotes. We extracted ~800 18S rRNA reads per sample (~22,000 reads per site) from 1455 samples from 495 plots across 45 NEON sites in 11 biomes, which corresponded to 5183 genera in 35 phyla. To our knowledge, this represents the first large‐scale soil eukaryote analysis of NEON data. We asked whether taxonomic richness paralleled patterns previously established ecological trends and found that eukaryotic richness was negatively correlated with pH, managed sites lowered eukaryotic richness by 47%, most biomes had a distinct eukaryotic community, and fire decreased eukaryotic richness. These findings parallel generally accepted ecological trends and support the notion that NEON soil metagenome datasets can and should be used to explore spatiotemporal patterns in soil eukaryote diversity, its association with ecosystem functioning, and its response to environmental changes in North America.

Thrombolites—clotted organo-sedimentary deposits—forming today preserve carbonate carbon isotope (δ13Ccarb) values that suggest that photosynthesis shifts the δ13C value of the local dissolved inorganic carbon pool, resulting in carbonate minerals with δ13Ccarb values 1‰−6‰ higher than that expected for carbonate minerals precipitated in equilibrium with ambient lake water. To test whether these signals are preserved in the geologic record, we analyzed the δ13Ccarb values of thrombolites from the Cretaceous to Eocene Sheep Pass Formation (Nevada, USA). We performed fabric-specific analyses of both the clot component (interpreted to reflect photosynthetically influenced precipitation) and adjacent matrix carbonate (interpreted to reflect abiotic precipitation) and find that the δ13Ccarb values of clot components are consistently offset to higher values than adjacent matrix. The mean offset (Δ13C) between the clots and matrix was +2.77‰ (±0.94, 2 s.e.). These Δ13C values are consistent with predictions by a model of diurnal carbon cycling driven by photosynthesis. Modeled Δ13C values also match the documented Δ13C values in modern lakes with thrombolites. We interpret that Sheep Pass Formation Δ13C values preserve signals related to both local and external influences on the dissolved inorganic carbon pool. Fabric-informed sampling can disentangle these two signals, allowing for more robust chemostratigraphy from microbialite archives in addition to identification of a biosignature of photosynthesis.

<strong class="journal-contentHeaderColor">Abstract.</strong> Vegetation phenology plays a significant role in driving seasonal patterns in land-atmosphere interactions and ecosystem productivity, and is a key factor to consider when modeling or investigating ecological and land-surface dynamics. To integrate phenology in ecological research ultimately requires the application of carefully curated and quality controlled phenological datasets that span multiple years and include a wide range of different ecosystems and plant functional types. By using digital cameras to record images of plant canopies every 30 minutes, pixel-level information from the visible red-green-blue color channels can be quantified to evaluate canopy greenness (defined as the green chromatic coordinate, <em>G_CC</em>), and how it varies in space and time. These phenological cameras (i.e., &ldquo;PhenoCams&rdquo;) offer a pragmatic and effective way to measure and provide phenology data for both research and education. Here, in this dataset descriptor, we present the PhenoCam dataset version 3 (V3.0), providing significant updates relative to prior releases. PhenoCam V3.0 includes 738 unique sites and a total of 4805.5 site years, a 170 % increase relative to PhenoCam V2.0 (1783 site years), with notable expansion of network coverage for evergreen broadleaf forests, understory vegetation, grasslands, wetlands, and agricultural systems. Furthermore, in this updated release, we now include a PhenoCam-based estimate of the normalized difference vegetation index (<em>cameraNDVI</em>), calculated from back-to-back visible and visible+near-infrared images acquired from approximately 75 % of cameras in the network, which utilize a sliding infrared cut filter. Both <em>G_CC</em> and <em>cameraNDVI</em> showed similar, but somewhat unique, patterns in canopy greenness and VIS vs. NIR reflectance, across various ecosystems, indicating their consistent ability to record phenological variability. However, we did find that at most sites, <em>G_CC</em> time series had less variability and fewer outliers, representing a smoother signal of canopy greenness and phenology. Overall, PhenoCam greenness as measured by both <em>G_CC</em> and <em>cameraNDVI</em> provides expanded opportunities for studying phenology and tracking ecological changes, with potential applications to the evaluation of satellite data products, earth system and ecosystem modeling, and understanding phenologically mediated ecosystem processes. The PhenoCam V3.0 data release is publicly available for download from the Oak Ridge National Lab Distributed Active Archive Center: the source imagery used to derive phenology information is available at <a href="https://doi.org/10.3334/ORNLDAAC/2364" target="_blank" rel="noopener">https://doi.org/10.3334/ORNLDAAC/2364</a> (Ballou et al., 2025), and the summarized phenology data are available at <a href="https://doi.org/10.3334/ORNLDAAC/2389" target="_blank" rel="noopener">https://doi.org/10.3334/ORNLDAAC/2389</a> (Zimmerman et al., 2025).

<strong class="journal-contentHeaderColor">Abstract.</strong> Vegetation phenology plays a significant role in driving seasonal patterns in land-atmosphere interactions and ecosystem productivity, and is a key factor to consider when modeling or investigating ecological and land-surface dynamics. To integrate phenology in ecological research ultimately requires the application of carefully curated and quality controlled phenological datasets that span multiple years and include a wide range of different ecosystems and plant functional types. By using digital cameras to record images of plant canopies every 30 minutes, pixel-level information from the visible red-green-blue color channels can be quantified to evaluate canopy greenness (defined as the green chromatic coordinate, <em>G_CC</em>), and how it varies in space and time. These phenological cameras (i.e., &ldquo;PhenoCams&rdquo;) offer a pragmatic and effective way to measure and provide phenology data for both research and education. Here, in this dataset descriptor, we present the PhenoCam dataset version 3 (V3.0), providing significant updates relative to prior releases. PhenoCam V3.0 includes 738 unique sites and a total of 4805.5 site years, a 170 % increase relative to PhenoCam V2.0 (1783 site years), with notable expansion of network coverage for evergreen broadleaf forests, understory vegetation, grasslands, wetlands, and agricultural systems. Furthermore, in this updated release, we now include a PhenoCam-based estimate of the normalized difference vegetation index (<em>cameraNDVI</em>), calculated from back-to-back visible and visible+near-infrared images acquired from approximately 75 % of cameras in the network, which utilize a sliding infrared cut filter. Both <em>G_CC</em> and <em>cameraNDVI</em> showed similar, but somewhat unique, patterns in canopy greenness and VIS vs. NIR reflectance, across various ecosystems, indicating their consistent ability to record phenological variability. However, we did find that at most sites, <em>G_CC</em> time series had less variability and fewer outliers, representing a smoother signal of canopy greenness and phenology. Overall, PhenoCam greenness as measured by both <em>G_CC</em> and <em>cameraNDVI</em> provides expanded opportunities for studying phenology and tracking ecological changes, with potential applications to the evaluation of satellite data products, earth system and ecosystem modeling, and understanding phenologically mediated ecosystem processes. The PhenoCam V3.0 data release is publicly available for download from the Oak Ridge National Lab Distributed Active Archive Center: the source imagery used to derive phenology information is available at <a href="https://doi.org/10.3334/ORNLDAAC/2364" target="_blank" rel="noopener">https://doi.org/10.3334/ORNLDAAC/2364</a> (Ballou et al., 2025), and the summarized phenology data are available at <a href="https://doi.org/10.3334/ORNLDAAC/2389" target="_blank" rel="noopener">https://doi.org/10.3334/ORNLDAAC/2389</a> (Zimmerman et al., 2025).

Increasing fine root carbon (FRC) inputs into soils has been proposed as a solution to increasing soil organic carbon (SOC). However, FRC inputs can also enhance SOC loss through priming. Here, we tested the broad-scale relationships between SOC and FRC at 43 sites across the US National Ecological Observatory Network. We found that SOC and FRC stocks were positively related with an across-ecosystem slope of 7 ± 3 kg SOC m−2 per kg FRC m−2, but this relationship was driven by grasslands. Grasslands had double the across-ecosystem slope while forest FRC and SOC were unrelated. Furthermore, deep grassland soils primarily showed net SOC accrual relative to FRC input. Conversely, forests had high variability in whether FRC inputs were related to net SOC priming or accrual. We conclude that while FRC increases could lead to increased SOC in grasslands, especially at depth, the FRC-SOC relationship remains difficult to characterize in forests.

Quantifying headwater stream carbon emissions is important for our understanding of the global carbon cycle because these emissions (an estimated 0.93-1.15 Pg C year) can be substantial compared to the terrestrial flux. Headwater stream networks can have high emissions due to their coupling with the terrestrial environment and high turbulence with some estimates predicting headwater stream networks can contribute 70% of the global riverine stream emissions. These carbon emissions are challenging to predict, especially with regards to headwater stream network spatiotemporal heterogeneity. The majority of headwater streams exhibit changes in stream network area on a seasonal basis, and these locations and extents are not often well documented because they are based on topographic maps with limited spatial accuracy. Research suggests 50-80% of river networks are comprised of non-perennial stream segments. &amp;#160;Physically based models are a potential solution to both mapping streamflow permanence and carbon dioxide emissions by accounting for the spatiotemporal heterogeneity that can occur in stream networks.In this study, we modeled stream permanence at three streams across the United States in different ecosystems using the Watershed Erosion Prediction Project (WEPP) hydrological model to simulate changes in stream network area over the year. We then used these results to inform a process-based stream network model to predict carbon emission from these networks throughout the year. We calibrated these network model with longitudinal data collected at the three sites during both low and high flow. Our results show the importance of considering stream permanence when predicting stream network carbon emissions, and how some ecosystems may emerge as hotspots for these emissions during high flow periods.