Journal of Geophysical Research: Biogeosciences最新文献

Sea level rise (SLR) and increased storm intensity are causing landward expansion of intertidal zones in the low-lying Delmarva Peninsula, allowing marsh migration into forests and agricultural fields. Transitional zones along the marsh-upland transects are visible aboveground as ghost forests and crop die-off, respectively. While the aboveground impacts of marsh migration are clear, the effects on belowground biogeochemistry are understudied. To characterize the impacts of marsh migration on soil biogeochemistry, we collected soil cores from marsh-upland transects at 3 agricultural and 3 forested sites along the Delmarva Peninsula. Soil cores were analyzed for both porewater chemistry and solid-phase characterization. Marsh end members support sulfate reduction; transitional zones support iron reduction; and upland end members support aerobic metabolisms at the surface, with iron reduction occurring at depth. In addition, the quality and quantity of dissolved organic matter changed across the transects, indicating differences in carbon source and cycling dynamics. Furthermore, our results show that soil carbon concentration varies drastically from lowland marsh to uplands, with marshes having 4–50 times more soil carbon than their upland endmembers. We also observed site-specific differences, where at the site with the lowest slope, the migrating marsh layer was relatively thin and was underlain by low-carbon aerobic soil that was coarser-textured. These findings have important implications for better understanding the incremental and belowground effects of SLR on coastal forests and agricultural lands.

Urban stormwater ponds (SWPs) are increasingly recognized as critical hotspots for carbon (C) and nitrogen (N) cycling, driven by high external inputs and elevated internal productivity. In developed watersheds, SWPs often replace natural aquatic ecosystems at equal or greater densities, but direct comparisons of the C and N dynamics between these engineered and natural ecosystems remain scarce. During distinct wet and dry seasons (Florida, USA), we compared the diffusive air-water flux and hypolimnetic saturation of carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O), as well as the composition of dissolved organic matter (DOM) between SWPs (n = 15) and naturally occurring clear (n = 3) and dark colored (n = 3) ponds in undisturbed watersheds. SWPs had similar CO₂ and lower CH₄ fluxes compared with natural-dark ponds, and higher fluxes than natural-clear ponds. Both natural pond types (clear and dark) were more stratified than SWPs, resulting in a greater CH₄ buildup in the natural ponds. N₂O fluxes were negligible across all pond types, with dark ponds being N₂O sinks. SWPs contained unique humic DOM not found in natural ponds, as well as microbially derived DOM that was similar to, and humic DOM that was identical to, that of natural ponds. Our study underscores that SWPs differ from natural ponds in greenhouse gas production and DOM composition, providing evidence that these rapidly emerging ecosystems alter C forms and greenhouse gas fluxes in developed landscapes.

δ¹³C in particulate organic carbon (POC), dissolved organic carbon (DOC), dissolved inorganic carbon (DIC), carbon dioxide (CO_2(g)) and methane (CH_4(g)), together with geochemical modeling, were applied to describe carbon cycle evolution in 40 boreal lakes situated across a permafrost thaw gradient in northeastern Alberta, Canada, where hydrological and geochemical trends had previously been established in a multi-decadal study. Progressive carbon cycle succession, characterized by enhanced allochthonous carbon loading, methanogenesis, methane oxidation, and alteration of in-lake DIC regulation, is found to progress in response to periodic water input increases associated with permafrost thaw, and has resulted in modification of the carbon cycle processes in post-thaw lakes. Hydrologic indicators, including water yield (WY), groundwater—surface water ratio (GW/SW), and tritium content appear to undergo evolution across the thaw gradient, and proceed consistently among softwater, circumneutral, and hardwater lakes, although site-specific differences in underlying organic versus inorganic carbon source balances are apparent. Progressive CO₂ supersaturation and CH₄ increases generally accompany permafrost thawing. Isotopic signatures suggest mainly acetoclastic methane production, found in previous studies to be common for newly-thawed peatlands, subsequently modified by methane oxidation in 50% of lakes. Alteration of hydrologic, geochemical and carbon cycling processes has important implications for understanding potential trajectories of climate-driven changes near the southern margin of the zone of discontinuous permafrost.

Dissolved organic matter (DOM) plays an important role in aquatic carbon cycling and is a valuable metric of ecosystem functioning and water quality in freshwater ecosystems. Despite its importance for biogeochemical cycling and water quality, no near-term iterative forecasts have previously been developed for freshwater DOM concentrations. To advance both our understanding of freshwater DOM dynamics and management, we developed 1–34 days-ahead forecasts of fluorescent DOM (fDOM) in three drinking water reservoirs. These temperate reservoirs are co-located in Virginia, USA and experience variable DOM dynamics (range: 5–27 QSU (quinine sulfate units)). We developed six different forecasting models to predict fDOM in each reservoir. Three models were time series models based on forecasted drivers (water temperature and meteorology) that were updated daily from high-frequency fDOM sensors. The other forecast models included a neural network machine learning model and two baseline reference models (day-of-year mean and persistence). Altogether, our forecasts were able to capture observed dynamics over a year in all three reservoirs, with one time series model outperforming the baseline models across the full 34-day forecast horizon. Aggregated across reservoirs and models over a year, forecast RMSE increased from 0.7 to 4.1 QSU over the 1–34 days-ahead forecast horizon. Forecast skill varied substantially across seasons, with greatest accuracy in the spring and winter compared to the summer and fall across reservoirs. These forecasts can help improve our understanding of the predictability of DOM and inform management in freshwater ecosystems as carbon dynamics become more variable due to global change.

Accurate estimates of soil total phosphorus (TP) concentrations are essential for sustainable nutrient management, food security, and water quality protection. This study predicts and maps the spatial distribution of TP in the top 5 cm and C horizon of soils across the conterminous USA (CONUS) using data from the Geochemical and Mineralogical Data for Soils of the Conterminous United States. We compare the performances of random forest (RF) and inverse distance weighting (IDW) to model and generate soil TP predictions. The RF incorporates 19 predictor variables, including spatial coordinates, climate, soil properties, and topography, while IDW relies solely on coordinates and interpolates between soil TP observations. Models are evaluated using five-fold cross-validation. The RF models outperform the IDW models and explain 52% (RMSE = 0.22 log₁₀ mg kg⁻¹) and 56% (RMSE = 0.26 log₁₀ mg kg⁻¹) of the variance in soil TP for the top 5 cm and C horizon, respectively. As expected, both model types identify higher TP concentrations in the top 5 cm than in the C horizon, particularly in agricultural regions, reflecting anthropogenic influences. Furthermore, the RF-generated maps show more realistic spatial patterns that capture the heterogeneity of the CONUS and avoid the bullseye patterns often characteristic of IDW-generated maps. Additional insights from the RF models show that coordinates, soil texture, pH, and climate are top predictors of soil TP. Increased availability of variables, such as iron and aluminum, that can bind with phosphorus in soils, could improve RF model performance.

Cover crops and agroforestry are gaining prominence as climate-smart agricultural practices, offering mitigation and adaptation benefits through enhanced carbon sequestration, improved soil health, and biodiversity conservation. However, the biogeophysical climate impacts from the changes they induce in the surface energy balance are insufficiently understood. This study uses a coupled atmosphere-land model and field-based observations to investigate the upper-bound climate impacts from large-scale global adoption of these practices. Replacing bare soil with cover crops in winter reduces near-surface air temperature by about −0.3 ± 0.11°C in snow-free regions, primarily due to increased latent heat flux. In snow-covered regions, temperature responses are more variable due to albedo changes. Leafier and taller non-leguminous crops induce stronger cooling than legumes under both snow-free and snow-covered conditions. Agroforestry induces year-round cooling in the tropics (up to −0.14 ± 0.05°C annually) and warming in most extratropical areas due to snow-albedo feedbacks. The biogeophysical warming outside the tropics can be offset by the cooling contributions from increased carbon sequestration in vegetation and soil, and in snow-free areas biogeophysical cooling reinforces these benefits. Climate effects remain similar even when cover crops or agroforestry are applied across twice the cropland area, highlighting nonlinear system responses and opportunities to optimize cooling benefits through selective deployment. These findings underscore the importance of accounting for both biogeophysical and biogeochemical processes when evaluating sustainable agricultural practices and their capacity to support a transition toward climate change resilient agricultural systems that align local adaptation needs with long-term global climate mitigation goal.

Bald cypress (Taxodium distichum (L.) Rich.), a foundation species in the bottomland hardwood forests of the southeastern United States, is essential for maintaining ecosystem functions. This study assesses the impacts of environmental changes on bald cypress ring widths and xylogenesis by correlating growth patterns with climatic variables, using ring-width data from 1775$��-�$2022 and cellular development data from the 2023 growing season. We collected biweekly cambium samples from five trees in western Alabama, analyzing the correlation between cell development and environmental factors such as air and water temperatures, precipitation, water levels, solar radiation, and day length. Results indicate that new cell growth is significantly influenced by day length (r² = 0.84, p 0.01) and maximum water temperature (r² = 0.59, p 0.01), with water temperature potentially playing a role in initiating the growing season, which typically starts in late May and ends by early September. Notably, an intense precipitation event in early July, delivering 7.34 cm of rain, coincided with a mid-season increase in cell production after trees started to decrease production following the summer solstice, underscoring the sensitivity of bald cypress to acute hydro-meteorological events. The cessation of growth corresponded with the drying of the site, indicating water availability as a possible factor for ending the growth phase. These findings underscore the complex interaction between bald cypress and its changing environment, providing insights into its adaptive strategies to climatic variability and highlighting the ecological importance of this species in forecasting and managing wetland resilience.

Interactions between aquatic organisms and contaminants in under-ice lake environments are poorly characterized relative to ice-free seasons. Here, we compared under-ice arsenic biogeochemical processes and plankton community composition in four subarctic lakes spanning an arsenic contamination gradient. We focused on a short (7–10 days) late winter transitional period where lakes began to thaw but were still ice-covered; a potentially significant but understudied period of rapid biological and chemical change. We observed decreases in conductivity over the late winter transitional period, due to snowmelt influx. We also observed decreases in ice thickness and increases in photosynthetically active radiation, dissolved oxygen, and water temperatures, which corresponded with a proliferation of under-ice phytoplankton. The two study lakes with the highest under-ice arsenic concentrations experienced a dilution in the surface waters following the influx of snowmelt, while meltwater was likely a source of arsenic enrichment to the study lake with the lowest under-ice arsenic concentration. The introduction of oxygen into surface waters also shifted the arsenic pool to a higher relative fraction of arsenate compared to arsenite for lakes experiencing under-ice anoxia. The organic arsenic pool increased at spring thaw onset in 2022, as would be expected based on the increase in biological activity, a result not replicated in 2023. Arsenite was a statistically significant driver of late winter variation in phytoplankton assemblages, but not for zooplankton or rotifers. Overall, this study generates new insights into under-ice arsenic cycling and ecotoxicity in subarctic lakes, which remain ice-covered for much of year.

Arctic permafrost soils store vast amounts of carbon (C)-rich organic matter that has accumulated due to low temperatures that suppress microbial decomposition. As Arctic warming intensifies, soil microbes become increasingly active, even while plant growth remains dormant. Seasonal decoupling between plant and microbial decomposer growth can accelerate carbon dioxide (CO₂) release from soils, however, most Earth system models underestimate cold-season C emissions and do not accurately represent the freeze–thaw transitions that govern microbial access to substrates during these critical periods. These model–data mismatches often stem from empirical formulations, such as using a fixed Q₁₀ functions to represent microbial respiration, an oversimplification of a complex interplay of temperature, moisture, and substrate diffusion. To address this, we incorporated explicit, temperature-dependent diffusional constraints on microbial activity, (the Dual Arrhenius Michaelis–Menten (DAMM) model), into the Stoichiometrically Coupled Acclimating Microbe–Plant–Soil (SCAMPS) model which uses the Q₁₀ function to represent microbial respiration. We used this enhanced model (SCAMPS_DAMM) to simulate Arctic ecosystem responses to a 50-year winter warming scenario and compared outcomes to the original SCAMPS framework. While both models predicted overall soil C losses under warming, SCAMPS_DAMM produced more constrained increases in microbial respiration and plant productivity. These differences led to similar total ecosystem C declines but divergent patterns of C and N allocation between plant and soil pools. Thus, incorporating mechanistic constraints on microbial access to substrates through explicit representation of temperature and moisture controls altered model projections of Arctic biogeochemical responses to climate change.

Urban lakeshore zones characterized by highly spatiotemporal heterogeneity serve as critical hotspots for N₂O emissions. While nosZI communities contribute to nitrous oxide (N₂O) sink potential, their regulatory mechanisms in the urban lakeshores remain unexplored. Here, we investigated the variations and driving patterns of N₂O fluxes and nitrogen (N) removal rates in continuously flooded (CF), semi-flooded (SF), and non-flooded (NF) zones of Pipa Lake in China during both summer and winter. Results revealed that CF zones exhibited seasonal N₂O sink–source shifts (−14.73 and 9.00 μg m⁻² hr⁻¹ for summer and winter, respectively) and higher potential denitrification rates (0.63−43.73 nmol N g⁻¹ hr⁻¹) compared to SF and NF zones. These shifts were likely driven by seasonal variations in nosZI community abundance and composition in CF zones, for example, significantly reduced nosZI gene abundance (P < 0.05), and remarkably enriched taxa Azospirizaceae (40.43%) in the winter. Additionally, sediment carbon and nitrogen substrates exerted stronger direct effects on N₂O emissions than indirect effects mediated by nosZI communities, while moisture regulated N₂O emissions by suppressing nosZI abundance and enhancing carbon substrates. Under projected urban expansion scenarios, the CF zone would be a potential hotspot for N₂O emissions, thereby exacerbating climate change. Consequently, managing sediment carbon and nitrogen substrates to modulate nosZI communities would present a viable strategy for N₂O mitigation in urban lakeshores.