Journal of Geophysical Research: Biogeosciences最新文献

Model representation of carbon uptake and storage is essential for accurate projection of the response of the arctic-boreal zone to a rapidly changing climate. Land model estimates of LAI and aboveground biomass that can have a marked influence on model projections of carbon uptake and storage vary substantially in the arctic and boreal zone, making it challenging to correctly evaluate model estimates of Gross Primary Productivity (GPP). To understand and correct bias of LAI and aboveground biomass in the Community Land Model (CLM), we assimilated the 8-day Moderate Resolution Imaging Spectroradiometer (MODIS) LAI observation and a machine learning product of annual aboveground biomass into CLM using an Ensemble Adjustment Kalman Filter (EAKF) in an experimental region including Alaska and Western Canada. Assimilating LAI and aboveground biomass reduced these model estimates by 58% and 72%, respectively. The change of aboveground biomass was consistent with independent estimates of canopy top height at both regional and site levels. The International Land Model Benchmarking system assessment showed that data assimilation significantly improved CLM's performance in simulating the carbon and hydrological cycles, as well as in representing the functional relationships between LAI and other variables. To further reduce the remaining bias in GPP after LAI bias correction, we re-parameterized CLM to account for low temperature suppression of photosynthesis. The LAI bias corrected model that included the new parameterization showed the best agreement with model benchmarks. Combining data assimilation with model parameterization provides a useful framework to assess photosynthetic processes in LSMs.

Submerged macrophytes are important indicators of the state of shallow freshwater ecosystems. Reconstruction long-term changes in submerged macrophytes remains a challenge in paleoecology. Here, the relative biomass (mass weight) of different plants to sedimentary organic matter in a shallow lake in central China was estimated using a Bayesian multi-source mixing model with concentrations and δ¹³C of n-alkanes extracted from surface lake sediments. The spatial distribution of submerged macrophytes biomass estimated by the model correlates with water transparency, water depth, and total nitrogen. The correlation patterns are consistent with previously established patterns of submerged macrophyte growth and water conditions, which supports the utility of the Bayesian approach in shallow freshwater lakes. In comparison, P_aq, proportion of mid-chain length (C23, C25) to long-chain length (C29, C31) homologs, underestimated the contribution of submerged macrophytes, especially in samples with moderate P_aq values (0.3 < P_aq < 0.4). On the other hand, some discrepancies between the model output and the satellite imagery estimated macrophyte coverage are present, which suggests that ground-truthing is needed to further evaluate this approach. Our study demonstrates that the Bayesian mixing model combining the abundance and isotopes of n-alkanes makes a reasonable estimation of the relative biomass of submerged macrophytes in the sediments. This approach provides new insights into reconstructing long-term variations in submerged macrophytes for paleoecological studies, which is valuable for the restoration and conservation of shallow freshwater lakes when long-term limnological monitoring is lacking.

Developing accurate landscape-scale aboveground biomass (AGB) maps is critical to understanding coastal deltaic wetland resilience, as AGB influences stability and elevation dynamics in herbaceous wetlands. Here we used AVIRIS-NG imaging spectrometer (or “hyperspectral”) data from NASA's 2021 Delta-X mission in coastal Louisiana to map seasonal changes in herbaceous AGB across two deltaic basins with contrasting sediment delivery and hydrologic regimes: the Atchafalaya (active) and Terrebonne (inactive). We assessed the impact of atmospheric effects on our retrievals, as high water vapor content in August 2021 caused significant noise in the 880–1,000 and 1,080–1,200 nm near-infrared (NIR) wavelengths. We hypothesized that correcting these wavelengths with our conditional Gaussian interpolation algorithm would improve AGB estimates due to their association with plant canopy water content. We empirically assessed the performance of the corrected spectra on AGB estimates using Partial Least Squares Regression (PLSR), finding that the corrected NIR bands attained high variable importance and reduced estimation errors. Our Random Forest regression approach based on the corrected spectra attained equivalent error metrics via leave-one-out-cross-validation as the PLSR models (R² = 0.43, mean absolute error = 257.3 g/m²) while greatly improving the AGB maps' visual quality, having better captured variability while reducing noise and discontinuities in AGB estimates across flightlines. The maps show differing seasonal growth, with the Atchafalaya and Terrebonne Basins' AGB increasing from means of 4.3–9.4 and 4.6–8.9 Mg/ha, respectively. We demonstrated that imaging spectroscopy can be applied to assess herbaceous biomass stocks, growth patterns, and resilience in coastal ecosystems.

Smoke from wildland fires contains more diverse, viable microbes than typical ambient air, yet little is known about the sources and sinks of smoke-borne microorganisms. Data from molecular-based surveys suggest that smoke-borne microorganisms originate from material associated with the vegetation and underlying soils that becomes aerosolized during combustion, however, the sources of microbes in smoke have not yet been experimentally assessed. To elucidate this link, we studied high-intensity forest fires in the Fishlake National Forest, Utah, USA and applied source-sink modeling to assemblages of 16S ribosomal RNA (rRNA) gene sequences recovered from samples of smoke, vegetation, and soil. Our results suggest that 70% of the bacterial taxa in smoke originated from the local aspen (Populus tremuloides) (33%) and soil (37%) communities. In comparison, 42% of bacteria in air sampled prior to the fires could be attributed to these terrestrial sources. When the bacterial assemblages in smoke were modeled as sources to the local communities, they contributed an average of 25% to the terrestrial sinks versus an estimated contribution of <4% from ambient air. Our results provide support for the role of wildland fire in bacterial dispersal and the working hypothesis that smoke is an environmental reservoir of microbes for receiving ecosystems.

Understanding the response of forests to the increases in atmospheric CO₂ (c_a) is fundamental to implementing innovative management strategies and for assessing impacts on the global carbon and water cycles. Here, we explored correlations between ecophysiological traits and climate variability that influence changes in stable isotope carbon and oxygen (δ¹³C and δ¹⁸O) of tree-rings. We present these relationships between species of the contrasting genera Abies and Pinus, along a latitudinal transect encompassing different biogeographical regions in North America. We also tested if the rate of intrinsic water-use efficiency per unit of c_a (dW/dc_a) during two periods (1890–1965 vs. 1966–2016), for fir and pine were different and indicated acclimation to c_a increases. We hypothesize that, spatially and temporally, the divergent responses among species to carbon and oxygen isotopes and dW/dc_a are influenced by the site conditions and the historical increases in c_a. From our results, we show that fir and pine species will behave physiologically different as global warming progresses. Firs are more responsive to atmosphere vapor pressure deficit along different geographical zones. The survival of forests species under climate change will rely on the response to water stress and species' traits that influence the regulation of dW. Finally, we want to highlight the concept of “progressive resource limitation” of soil water and nutrients, previously proposed by other authors, that likely indicate fir species that inhabit moister sites will benefit more from increased c_a than pine, but this positive effect is likely transitory as global warming increases.

Phytoplankton photosynthesis is the first step of energy capture in the open ocean and is therefore fundamental for global biogeochemical processes and ecosystem functioning. High-resolution methods are required to fully capture the variability of marine photosynthesis and its environmental drivers. Here, we combine two high-resolution underway methods to study phytoplankton photophysiology, Fast Repetition Rate fluorometry and Flow Cytometry, along a transect in the North-East Atlantic Ocean from the polar circle to the equator. Significant spatial distinctions in photophysiological strategies were found between biogeographical provinces. The most pronounced distinction was present between the subarctic North Atlantic and the oligotrophic subtropical gyre, where the latter was typified by high photosystem II (PSII) turnover rates, low pigment-to-cell volume ratios, low PSII quantum efficiency and low absorption cross sections for photochemistry in PSII. Small-scale variability along the transect results from varying diel cycles in photophysiology, possibly governed by light availability and cell metabolism. In general, we found that variability in PSII photochemistry was associated with variability in sea surface temperature, whereas the median mixed layer irradiance could explain more of the variation in the light harvesting capacity of the phytoplankton community. This implies that the expected climate change driven shoaling of the mixed layer may impact phytoplankton light harvesting strategies.

The effort to map terrestrial biodiversity, in recent years limited mostly to the use of broadband multispectral remote sensing at decameter scales, can be greatly enhanced by harnessing hyperspectral imagery. Interpretation of hyperspectral imagery may be aided by the Mixture Residual (MR) spectral preprocessing transformation. MR integrates the benefits of spectral mixture analysis with the absorption peak-enhancing characteristics of continuum removal. MR characterizes each pixel as a linear combination of generic end-members estimating the spectral continuum, from which the residual of each wavelength is computed and treated as a source of additional information. Using Hyperspectral Precursor of the Application Mission (PRISMA) imagery, we tested the ability of MR-transformed reflectance as compared to untransformed surface reflectance (SR) to map plant associations and land cover using ground truthing and random forest classifications across four landscapes within the North American Coastal Plain. We used a forward stepwise selection algorithm to choose bands for each classification and subsequently compared these between SR and MR. Our MR classifications distinguished land cover with 5% greater balanced accuracy on average than the SR-based classifications across all four landscapes. The MR-based classification that integrated data from all landscapes into a unified model encompassing all 21 land cover types achieved a 76% average balanced accuracy over three iterations. Generally, MR utilized the near-infrared region to a greater degree than SR while deemphasizing the green peak. Based on our results, MR improves the accuracy of mapping terrestrial biodiversity, likely extending to other current and planned satellite hyperspectral missions.

The export of dissolved organic matter (DOM) from rivers is essential for linking terrestrial and marine carbon reservoirs in the global carbon cycle. However, there is limited knowledge regarding how the molecular composition of riverine DOM changes under different hydrological conditions, especially during extreme rainfall events. Moreover, the factors beyond hydrology that impact DOM composition have not been well defined. To address these gaps, samples were collected from a human-impacted medium-sized subtropical monsoonal river across various hydrological conditions throughout a complete hydrological cycle. Utilizing high-resolution mass spectrometry, it was discovered that the solid-phase extractable DOM (SPE-DOM) during the high-flow (1 < runoff (Q): annual mean runoff (Q_m) < 3) and extreme-rain (Q:Q_m > 3) periods exhibited a higher number of molecular formulae, lower H/C, higher O/C, and a higher proportion of carboxylic-rich alicyclic molecules compared to the low-flow period (LFP) (Q:Q_m < 1). These alterations were attributed to input from more diverse sources, particularly a greater input from soil organic matter with higher oxidation degrees. Additionally, the P-containing formulae were more enriched during the extreme-rain period, likely from agricultural lands and sediment release. Conversely, the fraction of S-containing formulae was significantly higher during the LFP, possibly due to the amplified influence of anthropogenic input. Furthermore, the DOM aromaticity did not fluctuate with runoff but was significantly associated with temperature. In summary, the study indicated that the composition of DOM varied significantly under different hydrological conditions, with temperature and anthropogenic activities identified as crucial factors influencing riverine DOM export.

Diatom communities preserved in sediment samples are valuable indicators for understanding the past and present dynamics of phytoplankton communities, and their response to environmental changes. These studies are traditionally achieved by counting methods using optical microscopy, a time-consuming process that requires taxonomic expertise. With the advent of automated image acquisition workflows, large image data sets can now be acquired, but require efficient preprocessing methods. Detecting diatom frustules on microscope images is a challenge due to their low relief, diverse shapes, and tendency to aggregate, which prevent the use of traditional thresholding techniques. Deep learning algorithms have the potential to resolve these challenges, more particularly for the task of object detection. Here we explore the use of a Faster Region-based Convolutional Neural Network model to detect siliceous biominerals, including diatoms, in microscope images of a sediment trap series from the Mediterranean Sea. Our workflow demonstrates promising results, achieving a precision score of 0.72 and a recall score of 0.74 when applied to a test set of Mediterranean diatom images. Our model performance decreases when used to detect fragments of these microfossils; it also decreases when particles are aggregated or when images are out of focus. Microfossil detection remains high when the model is used on a microscope image set of sediments from a different oceanic basin, demonstrating its potential for application in a wide range of contemporary and paleoenvironmental studies. This automated method provides a valuable tool for analyzing complex samples, particularly for rare species under-represented in training data sets.