Journal of Geophysical Research: Biogeosciences最新文献

Denitrification and anaerobic ammonium oxidation (anammox) are key processes in mitigating nitrogen (N) pollution in river ecosystems. However, there has been insufficient investigation of the riverine N-removal processes on the Qinghai-Tibet Plateau (QTP). This study investigated the driving mechanisms of denitrification and anammox in 23 small rivers using multiple approaches (remote sensing, ¹⁵N pairing, and molecular techniques). The rivers span large elevational and climatic gradients, offering an ideal window for studying the riverine N-removal processes, especially in the context of global change. The denitrification rates were 1.37 ± 2.88 mg N/kg/d in summer and 0.21 ± 0.26 mg N/kg/d in winter, which dominated the total riverine N removal (96% and 73%, respectively). Structural equation models (SEM) revealed that geographic factors (elevation and land use), water properties (water temperature, pH, etc.), sediment parameters (moisture, NO₃⁻-N, NH₄⁺-N, etc.), and microbial gene abundances collectively explained over 60% of the variation in N-removal rates, with sediment properties being the primary regulating factors in summer. In winter, SEM showed that the contribution of geographic factors increased. We proposed a framework combining cross-perspective methods for revealing in-river N removal mechanisms and predicted that, under global change, N removal processes in rivers on the QTP are likely to intensify in the future. The integrated approaches systematically offered critical insights into the processes and drivers of biogeochemical N cycling on the QTP.

Environmental observatory networks are fundamental in advancing scientific understanding of biogeochemical processes. FLUXNET is a global network of regional eddy covariance networks that measure ecosystem-scale exchanges of greenhouse gases (e.g., CO₂, CH₄, H₂O) and energy between the biosphere and the atmosphere. MexFlux is the eddy covariance network of Mexico, a megadiverse country with many underrepresented ecosystems within FLUXNET. This study evaluates the spatial representativeness of MexFlux by assessing its ability to capture the statistical and spatial heterogeneity of annual gross primary productivity (GPP) and evapotranspiration (ET) within Mexico. We tested four network configurations: the historical distribution of MexFlux sites (MexFlux-H, n = 33), an expanded network with 20 additional sites (MexFlux + 20, 53 sites), MexFlux sites with publicly available data (MexFlux-P, n = 20), and a hypothetical optimized design with only 25 sites (MexFlux25, n = 25). Results show that MexFlux-H and MexFlux-P overrepresent regions with GPP values between 250 and 600 gC m⁻² yr⁻¹ and ET of 200–1,200 mm yr⁻¹. MexFlux + 20 demonstrates that adding 20 strategically located sites improves the representativeness of MexFlux while preserving the historical distribution of the network. The configuration of MexFlux25 highlights that a few but strategically distributed sites are an alternative way to enhance the representativeness of the network. Mountain regions, tropical forests, and urban sites may remain underrepresented in any network configuration, highlighting the challenges of monitoring efforts in this country. Our framework integrates distributions, copulas, semivariograms, and upscaling to highlight the value of a multidimensional assessment of spatial representativeness, which applies to other regional FLUXNET networks.

Ocean alkalinity enhancement (OAE) is a marine carbon dioxide (CO₂) removal strategy that relies on lowering the ocean's pCO₂ via the addition of alkaline materials to facilitate enhanced CO₂ uptake with the potential for durable, long-term, storage. This strategy has gained recent scientific and private sector attention as a possible component of climate mitigation portfolios, yet many research questions remain. This work describes an analysis of historical reconstructions of regional carbonate chemistry developed via application of machine learning algorithms to an ocean reanalysis product. Model skill assessment demonstrated excellent performance when compared to regional observations, and this work focuses on four carbonate system variables that may influence OAE applications: total scale pH, calcite saturation state, the theoretical molar change in dissolved inorganic carbon associated with a molar change in total alkalinity (ΔDIC/ΔTA), and the timescale of CO₂ equilibrium of the surface mixed layer ($��{\tau }_{{\text{CO}}_2}�$). These metrics were combined into a suitability index to quantify locations and times of year more favorable for OAE. Much of the US Northeast Shelf and Slope region has seasonally similar suitability for small-scale OAE applications, with nearshore environments exhibiting high suitability year-round. Lagrangian particle tracking experiments show strong reductions in ΔDIC/ΔTA and increases in $��{\tau }_{{\text{CO}}_2}�$ due to horizontal and vertical transport, suggesting that when water motion is accounted for, reduced efficiency and longer equilibration times may impact successful observations of carbon uptake and storage. This analysis and framework were developed with publicly available tools, data sets, and global data products allowing for global scalability and application.

Nitrate discharge via stream water is a key indicator of how disturbances influence forest nutrient cycling. In a temperate forest in central Japan, 35 years of hydrological, biogeochemical, and plant uptake monitoring revealed sustained nitrate leaching for over 25 years following red pine dieback due to pine wilt disease in 1989–1990. Stream nitrate concentrations followed three distinct phases: (I) a rapid rise and decline (1990–2000), (II) a gradual decrease (2000–2010), and (III) a slight increase stabilizing above pre-dieback levels (post-2010). Phase I was driven by changes in soil nitrate production following pine mortality. In Phase II, prolonged low rainfall accelerated declines in NO₃⁻ concentrations. During Phase III, natural mortality of stressed cypress trees (2008–2015) coincided with a decline in nitrogen uptake of up to 65.7 kg N ha⁻¹ yr⁻¹, likely contributing to renewed nitrate leaching. Field experiments demonstrated that plant growth conditions strongly influenced catchment-scale nitrogen dynamics. Shifts in soil microbial activity were associated with climate variability and changes in vegetation. Notably, the effects of plant mortality on nitrate leaching were delayed, with several years separating disturbance events and hydrological responses. These findings highlight the importance of long-term monitoring to capture delayed and complex responses of nutrient cycling to ecosystem disturbances, integrating approaches such as dual isotope analyses, biogeochemistry, hydrology, and vegetation uptake.

Climate change is now a reality, one that requires and will continue to require robust scientific information to guide mitigation and adaptation policies. Although this is true for all countries worldwide, low- and middle-gross domestic product countries (LMGDP) face major technical, institutional, and financial barriers to producing such knowledge. These constraints limit their ability to generate greenhouse gas (GHG) budgets, define clear mitigation pathways, and design robust adaptation strategies. In contrast, freely available global scientific data are increasingly abundant. Open-access resources from a wide range of institutions provide high-quality climate and socioeconomic information at no cost. Leveraging these resources, combined with targeted capacity building and equitable international partnerships, offers a pathway to reduce research costs, avoid duplication, and strengthen policy-relevant science in these countries. Addressing the paradox of global data abundance versus local data scarcity requires not only open data but open collaboration that integrates global expertise with local knowledge. In this commentary, two critical topics are employed to illustrate this challenge: (a) the calculation of reliable national GHG budgets essential for effective mitigation and (b) the development of adaptation pathways within nationally determined contributions. Strengthening national capacities while harnessing global data sets represents a crucial step toward enabling LMGDP countries to respond effectively to climate change.

Recent advancements in remote sensing imagery classification have greatly improved monitoring of land use/land cover (LULC) dynamics, deepening our understanding of their effects on ecosystems and terrestrial nutrient cycling. Forecasting LULC change remains challenging because it is strongly influenced by socioeconomic drivers and biogeochemical processes linked to land management and climate change. To address this complexity, a wide range of models has been developed, from process-based to statistical approaches. Yet, comparisons at regional and global scales reveal large discrepancies, underscoring the need for more consistent calibration and validation with historical observations. Here, we leverage the increasing availability of annual LULC maps to evaluate the temporal performance of two independent data-driven approaches: ARIMA time-series forecasting and a deterministic Lotka–Volterra ecological-inspired model, across the Río de la Plata Grasslands, a threatened South American ecosystem. Both methods outperformed memoryless Markov chain models in capturing annual LULC transitions without requiring time-consuming processing spatial inputs. These results demonstrate that incorporating long-term annual LULC histories can substantially improve predictive skill and provide a robust framework for model intercomparison, with clear implications for linking land-cover change to ecosystem and Earth system modeling.

Reliable paleotemperature proxies are essential for reconstructing past climate. To refine interpretation of the MBT'_5ME index, based on bacterial brGDGT lipids, a year-long study was conducted in Rotsee, Switzerland, a seasonally stratified lake with a 4–21°C temperature range. Suspended particulate matter was collected monthly from the epilimnion and the hypolimnion, complemented by surface sediments and surrounding soils. Both intact polar (IPL) and core lipid (CL) brGDGTs were analyzed alongside 16S rRNA gene data to disentangle environmental (temperature, dissolved oxygen, and pH) and biological (microbial community) controls on brGDGT compositions. In the stratified epilimnion, MBT'_5ME values showed a muted response to summer warming (r = 0.59, p < 0.1), whereas the isomer ratio (IR) correlated more strongly with temperature (r = 0.68, p < 0.05). MBT'_5ME and IR were also significantly correlated (r = 0.93, p < 0.0001), providing a novel diagnostic tool to identify sedimentary GDGTs derived from surface waters. In the seasonally anoxic hypolimnion, MBT'_5ME correlated with pH (r = 0.79, p < 0.01) and IR with dissolved oxygen (r = −0.65 and p < 0.05). Microbial DNA analysis revealed low Acidobacterial abundances (<0.4% of reads), suggesting MBT'_5ME patterns are not solely driven by this phylum. Instead, hypolimnion IPL-brGDGTs correlated with gene abundance of several other bacteria, indicating broader microbial contributions. Surface sediments reflected an integrated water column signal, while also showing evidence for additional in situ IPL-brGDGT production. Overall, findings demonstrate that stratification onset drives MBT'_5ME variability, while epilimnion temperature exerts stronger control on IR, refining their application in paleoclimate proxies.

Wetlandscapes—networks of hydrologically connected wetlands—can influence the transport and transformation of nutrients across watersheds. As climate change and human activity reshape wetland extent and connectivity, these landscape-scale processes are being altered in ways that may intensify eutrophication in downstream lakes. We used Landsat-derived inundation data (1984–2020) to evaluate how long-term changes in wetlandscape properties have affected nutrient loading and phytoplankton bloom dynamics in Lake Winnipeg, Canada. Over this period, wetlands generally increased in number and size and exhibited greater connectivity to rivers and the lake but with declines observed after ∼2015. These changes coincided with periods of substantial increases in the magnitude and spatial extents of phytoplankton blooms followed by declines in 2015 in the North Basin. Sub-watersheds with shorter runoff travel distances to the lake showed stronger relationships between wetland connectivity and bloom metrics (p ≤ 0.10), suggesting reduced opportunity for nutrient retention and transformation. Incorporating runoff travel distance into wetlandscape assessments improved correlations with nutrient inputs and bloom extent. Rising surface temperatures further contributed to bloom intensification. These findings highlight how climate-driven changes in wetland connectivity influence lake nutrient dynamics and demonstrate the potential for globally available satellite data to support spatially targeted water quality management.

Crown-group eukaryotes diversified rapidly in the late Mesoproterozoic (ca. 1.1–1.0 Ga), but their evolution was slowed in the early Neoproterozoic (ca. 1.0–0.8 Ga), with a significant episode of diversification occurring again around 0.8 Ga. Previous studies suggest nutrient (nitrogen and phosphorus) scarcity in the seawater may have delayed eukaryotic evolution during this period. However, the influence of marine redox conditions on the evolution, either directly or indirectly, remains unclear. Applying integrated approaches of sedimentology, mineralogy and geochemistry, we analyze the shallow marine carbonates from the Zhaowei, Niyuan, and Jiudingshan formations of the Huaibei Group (ca. 1.0–0.95 Ga) in the southeastern North China to constrain the redox conditions of the early Neoproterozoic seawater. Microscopic observations revealed abundant water-column carbonate mud (“whiting”) precipitates in these formations, indicating moderately oxygenated surface waters. Geochemical results show that the I/(Ca + Mg) values of the carbonates are mostly (98%) below the Precambrian background baseline (0.5 μmol/mol), without significant negative Ce anomaly (0.82 ± 0.06, n = 44). These findings support low-oxygen conditions in the shallow waters. The combination of low oxygen conditions and nutrient scarcity likely delayed the Neoproterozoic diversification of early crown-group eukaryotes. This study provides valuable insights into marine redox conditions in Earth's middle age and their potential impact on early eukaryotic evolution.

Rapidly melting Arctic glaciers deliver increasing amounts of allochthonous material to the coastal ocean, altering carbon cycling and promoting heterotrophy. As key factors influencing the activity of heterotrophic microbes, the quantity and quality of Arctic coastal organic carbon warrant closer examination. We investigated the molecular composition of dissolved organic matter (DOM) in two rivers and surface waters of Young Sound, NE Greenland—a high Arctic fjord where glacial runoff contributes to low primary productivity and increasing heterotrophy. Using ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-qTOF-MS), we conducted a non-targeted analysis of solid-phase extracted DOM (SPE-DOM). We expected DOM composition to differ between the two studied rivers (Tyroler and Zackenberg), which contrast in length, glacial water source, and catchment characteristics, and to reflect the salinity gradient in fjord waters. Both rivers carried a strong glacial imprint, with DOM enriched in aliphatic constituents typically associated with higher bioavailability, yet the proglacial Zackenberg River also exhibited unique compositional features that were more unsaturated and aromatic in character. Comparisons along a salinity gradient, from river plumes to outer fjord and open sea, revealed limited contrasts beyond the most glacially influenced section, with SPE-DOM composition showing high similarity across sites. Although multiple factors may contribute to this similarity, dilution and rapid processing of glacially derived DOM are likely to play a role. While further research is needed to understand carbon cycling in high Arctic fjords, our findings offer relevant insight into the molecular characteristics and potential ecological roles of DOM in this environment.