New bioavailable nitrogen (N) from biological nitrogen fixation (BNF) is critical for the N budget and productivity of marine ecosystems. Nitrogen-fixing organisms typically inactivate BNF when less metabolically costly N sources, like ammonium (NH4+), are available. Yet, several studies observed BNF in benthic marine sediments linked to anaerobic sulfate-reducing bacteria (SRB) and fermenting firmicutes despite high porewater NH4+;concentrations (10-1,500 μM), making the importance of and regulating controls on benthic BNF unclear. Here, we evaluate BNF sensitivity to NH4+ in model anaerobic diazotrophs, the sulfate-reducer Desulfovibrio vulgaris var. Hildenborough and fermenter Clostridium pasteurianum strain W5; in sulfate-reducing sediment enrichment cultures, and in sediment slurry incubations from three Northeastern salt marshes (USA). BNF in sulfate-reducing cultures and sediments is highly sensitive to external NH4+, with a threshold for BNF inhibition of [NH4+] < 2 μM in cultures and < 9 μM in sediment slurries. The prevalence of SRB-like sequences in sediment-derived nitrogenase (nifH) genes and transcripts in this and other studies of benthic BNF along with an analysis of benthic NH4+ porewater data suggests a broad applicability of the inhibition thresholds measured here and the confinement of benthic BNF to surficial sediments. The timing of inhibition, fast NH4+ drawdown, and sediment heterogeneity are factors that can complicate studies of benthic BNF sensitivity to NH4+. We propose a simple theoretical framework based on the affinity of the NH4+ transporter to explain NH4+ control of BNF and improve biogeochemical models of N cycling.
The Southern Ocean (SO) is the worlds largest high nutrient low chlorophyll region, and has a plentiful supply of underutilised macronutrients due to light and iron limitation. These macronutrients supply the rest of the neighboring ocean basins, and are hugely important for global productivity and ocean carbon sequestration. Vertical mixing rates in the SO are known to vary by an order of magnitude temporally and spatially, however there is great uncertainty in the parameterization of this mixing, including in the specification of a background value in coarse resolutation Earth System Models. Using a biogeochemical-ocean model we show that SO biomass is highly sensitive to altering the background diapycnal mixing over short timescales. Increasing mixing enhances biomass by altering key biogeochemical and physical parameters. An increased surface supply of iron is responsible for biomass increases in most areas, demonstrating the importance of year round diapycnal fluxes of iron to SO surface waters. These changes to SO biomass could potentially alter atmospheric CO2 concentration over longer timescales, alluding to the importance of accurate representation diapycnal mixing in climate models.
Dinitrogen (N2) fixers (diazotrophs) fuel primary productivity by providing reactive nitrogen into the ocean ecosystem and promoting CO2 sequestration. N2 fixation has been extensively studied in the low latitudes of the Atlantic and Pacific Oceans. By comparison, the Indian Ocean remains the least explored and most enigmatic ocean basin. This is particularly the case for the Southern Indian Ocean (SIO). Here we explore N2 fixation activity and diazotroph community composition, diversity, and abundance from 20 to 60ºS in the SIO. While this region plays a key biogeochemical role serving as a link between the Atlantic and South Pacific Ocean waters, its N2 fixation potential remains unknown. Our results provide new insights into diazotrophy in a poorly studied region and expand the range of biomes where diazotrophy may be observed.
More and more applications of electrical resistivity tomography (ERT) for cylindrical objects have been rising in recent decades. This paper presents a 2.5-dimensional differential resistivity reconstruction scheme of cylindrical objects. The forward modeling algorithm incorporates the modified optimization wavenumbers to achieve an accurate 2.5-dimensional forward modeling. The modified optimization wavenumber selection is based on the approximate analytic solution of the circumference potential distribution of an infinitely long homogeneous cylindrical model, making it more accurate for cylindrical objects compared to the traditional optimization wavenumber selection which is only applicable for the half-space condition. In the laboratory, we measured the resistivity and resistance distributions of the sodium chloride solution-filled cylindrical tanks with/without a high resistivity rubber bar in the central. The modified and traditional optimization wavenumbers are included respectively to calculate the resistance distribution of the measured objects. The comparison results between the calculated and measured resistance distribution show that the modified optimization wavenumbers proposed in this paper can obtain higher calculation accuracy. The differential ERT incorporating the modified optimization wavenumbers is then employed to reconstruct the resistivity distribution of the cylindrical objects. The inversed resistivity values are in good agreement with the measured values. We, therefore, conclude that the modified optimization wavenumbers can result in better accuracy than the traditional one and the proposed 2.5-dimensional differential resistivity reconstruction scheme is time-saving and has great promise for the imaging of cylindrical objects.
The ribosome is a universal molecular machine (comprised of RNA and proteins) which translates the message from the genome into proteins (polymers of amino acids) in biology. Similar to how Flight and Cockpit voice recorders record and preserve an aircraft’s flight history, the ribosome has recorded signatures of its evolution. Tapping this resource is important for understanding the origins of life. The electrostatic properties/net positive charge(s) of ribosomal proteins (RPs) stabilize interactions with the negatively charged ribosomal RNA (rRNA) and influence the assembly and folding of ribosomes. A high percentage of RPs from extremely halophilic archaea are known to be acidic/negatively charged. Recently the net charges (at pH 7.4) of the RPs from a highly conserved cluster of RPs were found to have an inverse relationship with the halophilicity/halotolerance (ability to survive under salt conditions) levels in bacteria and archaea. In non-halophilic bacteria, these RPs are generally basic, contrasting with the acidic proteomes of the extreme halophiles. We explore the use of a new mathematical modeling technique based on interaction graphs to provide a systematic understanding of the structural differences in the Large Subunit (LSU) of the bacterium Escherichia coli and that of the extremely halophilic archaeon Haloarcula marismortui.
Snow and ice melt processes on the Greenland Ice Sheet are a key in Earth’s energy balance and hydrological cycle, and they are acutely sensitive to climate change. Melting dynamics are directly related to a decrease in surface albedo, amongst others caused by the accumulation of light-absorbing particles (LAPs). Featuring unique spectral patterns, these accumulations can be mapped and quantified by imaging spectroscopy. In this contribution, we present first results for the retrieval of glacier ice properties from the spaceborne PRISMA imaging spectrometer by applying a recently developed simultaneous inversion of atmospheric and surface state using optimal estimation (OE). The image analyzed in this study was acquired over the South-West margin of the Greenland Ice Sheet in late August 2020. The area is characterized by patterns of both clean and dark ice associated with a high amount of LAPs deposited on the surface. We present retrieval maps and uncertainties for grain size, liquid water, and glacier algae concentration, as well as estimated reflectance spectra for different surface properties. We then show the feasibility of using imaging spectroscopy to interpret multiband sensor data to achieve high accuracy, fast cadence observations of changing snow and ice conditions. In particular, we show that glacier algae concentration can be predicted from the Sentinel-3 OLCI impurity index with less than 10 % uncertainty. Our study evidence that present and upcoming orbital imaging spectroscopy missions such as PRISMA, EnMAP, CHIME, and the SBG designated observable, can significantly support research of melting ice sheets.
From extracting nutrients to releasing energy, biological metabolism plays an integral role in determining evolutionary patterns of organisms through geologic time. A previous study depicted a positive relationship between metabolic rate and extinction probability for Mollusca within the Neogene period. We hypothesized that this relationship extends to other metazoan phyla during the Cenozoic Era. Using specific respiration rates measured from living organisms and body size data for fossil taxa, we estimated metabolic rates of animals across different phyla: Arthropoda, Brachiopoda, Echinodermata, and Mollusca. This analysis was performed at the class level by using the classes with the most data available to represent each phylum: Malacostraca, Ostracoda, Cirripedia, Rhynchonellata, Echinoidea, Bivalvia, Cephalopoda, and Gastropoda. We then used logistic regression to estimate the relationship between the calculated metabolic rates and extinction probability during each epoch of the Cenozoic Era. Results indicate that while each individual phylum has a different extinction probability across each epoch, the regression coefficients for the combination of all studied phyla illustrate no relationship since there is not enough evidence to reject the null hypothesis of no relationship between metabolic rate and extinction probability. Although this means that there is no significant correlation for most of the phyla, there are some exceptions where metabolism does affect extinction probability. During the Oligocene epoch, animals within the Mollusca phylum portray a clear negative correlation between metabolic rate and extinction probability. A negative relationship is also observed for Echinoderms during the Eocene epoch. Despite the crucial role that metabolism plays in species survival, our results indicate that more information is needed regarding specific environmental conditions in order to accurately predict the factors that ultimately affect species survival across marine animals within the Cenozoic Era.
The Plankton, Aerosol, Cloud, and Ocean Ecosystem (PACE) mission is NASA’s next great investment in Earth Science, continuing NASA’s legacy of over forty years of satellite ocean color measurements. PACE, expected to launch in 2023, will advance our Earth-observing and monitoring capabilities through hyperspectral imaging and multi-angle polarimetric observations of ocean, atmosphere, and land ecosystems. PACE will give us an unprecedented view of our home planet and will support user-driven environmental applications through research and applied science to address societal challenges and inform decision-making. An integral component of actionable applied science is Design Thinking - an iterative, problem-solving framework that integrates human perspectives, needs, and experiences at every step of process. In this session, we will present the design process, collaborative activities, and outcomes of the 2021 PACE Applications Water Quality community focus session. A Design Thinking methodology was used in event planning as well as during day-of ideation breakout sessions. To foster empathy and better illuminate the goals, concerns, and needs of the diverse PACE user community, eight draft user personas were created to represent a range of water industry users from research to government to the private sector. Attendees worked together to complete the various personas by identifying different user challenges and pain points, ideal data experiences, and realistic, tailored Earth Observation and PACE Mission specific solutions and opportunities to satisfy users’ needs and goals. As a result, the eight archetype personas and co-production of knowledge will help ensure that PACE data are usable and accessible for a variety of possible users, thereby expanding the eventual reach and societal benefit of PACE. Lastly, we will highlight how Design Thinking will inform future stakeholder engagement efforts and actionable science via the PACE Mission.
Experimental studies of the interactions between biomolecules and minerals under conditions simulating harsh planetary environments provide key insights into possible prebiotic processes and the search for life. Despite protection from cosmic rays, UV, and oxidative degradation, buried biosignatures may undergo diagenetic processes that decrease the concentration of organic matter. Additionally, other degradation mechanisms occur as a result of elevated temperatures, pressures, mineral-organic interactions, and fluid/brine processes. In this study, we aim to provide a fuller understanding of preservation potential by considering several variables, including pressure, temperature, the mineral matrix environment, and fluid chemistry (salinity, pH, composition). This research expands previous anhydrous work to investigate the influence of lower pressure regimes, especially in a combined fluid/brine environment with various mineral matrices. To test the preservation potential of various biomolecules, we subjected samples to temperature, pressure, fluid, and mineral matrix conditions representative of different environmental stressors. The starting materials included: 1) isolated organic compounds added to various mineral standards, 2) An endolithic and microbe-rich natural calcite deposited from a CO2-rich hot spring, 3) cyanobacteria necromass. Experiments were conducted in three different devices 1) a piston-cylinder press reaching up to 15 kbar and 550 °C, 2) high-volume batch reaction vessels generating up to 15 MPa pressure and 80 °C, and 3) ambient pressure, high temperature furnaces. Samples were analyzed by GC-MS and LC-MS, while ICP-MS, XRD, and Raman were used for additional characterization. The influence of pressure can be clearly identified. Similarly, fluid transport, complex thermal degradation, and oxidation mechanisms are identified.
The relationship between dissolved solute concentration (C) and discharge (q) in streams, i.e., the C-q relationship, is a useful diagnostic tool for understanding biogeochemical processes in watersheds. In the ephemeral glacial meltwater streams of the McMurdo Dry Valleys [MDVs], Antarctica, studies show significant chemostatic relationships for weathering solutes and NO3-. Dissolved organic carbon (DOC) concentrations here are low compared to temperate streams, in the range of 0.1 to 2 mg C L-1, and their chemical signal clearly indicates derivation from microbial biomass. Many MDV streams support abundant microbial mats, which are also a source of organic matter to underlying hyporheic sediments. We investigated whether the DOC generation rate from these autochthonous organic matter pools was sufficient to maintain chemostasis for DOC despite these streams’ large diel and interannual fluctuations in discharge. To evaluate the DOC-q relationship, we fit the long-term DOC-q data to two models: a power law and an advection-reaction model. By using model outputs and other common metrics to characterize the DOC-q relationship, we found that this relationship is chemostatic in several MDV streams. We propose a conceptual model in which hyporheic carbon storage, hyporheic exchange rates, and net DOC generation rates are key interacting components that enable chemostatic DOC-q behavior in MDV streams. This model clarifies the role of autochthonous carbon stores in maintaining DOC-q chemostasis and may be useful for examining these relationships in temperate systems, where autochthonous organic carbon is readily bioavailable but where its signal is masked by a larger allochthonous signal.
Plant growth and development is impacted by the ability to capture resources including sunlight, determined in part by the arrangement of plant parts throughout the canopy. This is a very complex trait to describe, but has a major impact on downstream traits such as biomass or grain yield per acre. Though some is known about genetic factors contributing to leaf angle, maturity, and leaf size and number, these discrete traits do not encompass the structural complexity of the canopy. In addition, modeling and prediction for plant developmental traits using genomics or phenomics are usually conducted separately. We have developed proof-of-concept models that incorporate spatio-temporal factors from drone-acquired LiDAR features in a maize diversity panel to predict plant growth and development over time to improve our understanding of the biology of canopy formation and development. Briefly, voxel models for probability of beam penetration into the foliage were generated from 3D LiDAR scans collected at seven dates throughout crop canopy development. From the same plots, key architectural features of the maize canopy were measured by hand: stand count; plant, tassel, and flag leaf height; anthesis and silking dates; ear leaf, total leaf, and largest leaf number; and largest leaf length and width. We develop a self-supervised autoencoding neural network architecture that separately encodes plant temporal growth patterns for individual genotypes and plant spatial distributions for each plot. Then, leveraging the resulting latent space encoding of the LiDAR scans, we train and demonstrate accurate prediction of hand-measured crop traits.
Current methods of root sampling typically only obtain small or incomplete sections of root systems and do not capture their true complexity. To facilitate the visualization and analysis of entire, full sized root systems of crop plants, mesocosm growth containers were developed with an internal volume of 45 ft3 (1.27 m3). Mesocosms allow for unconstrained root growth, excavation and preservation of 3-dimensional RSA, and modularity that facilitates the use of a variety of sensors. Sensors arrays monitoring matric potential, temperature and CO2 levels are buried in a grid formation at depths of 1.25, 2.75, & 4.25 ft to assess environmental fluxes at regular intervals. Additionally, 3-dimensional water availability can be measured using ERT inside of root mesocosms. Methods of 3D data visualization of fluxes were developed to allow for comparison with root architectural traits. Following harvest, the recovered root system can be digitally reconstructed through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. Initial metrics inferred from the 3D models include root system biomass (occupied voxels), volume, flatness, convex hull volume and solidity with depth. Root systems are finally dissected and biomass measurements are made in a 3-dimensional matrix of the growth zone, while the crown is saved for X-ray CT analysis.
In both natural and built environments, microbes on occasions manifest in spherical aggregates instead of solid-affixed biofilms. These microbial aggregates are conventionally referred to as granules. Cryoconites are mineral rich granules that appear on glacier surfaces and are linked with expanding surface darkening, thus decreasing albedo, and enhanced melt. The oxygenic photogranules (OPGs) are organic rich granules that grow in wastewater with photosynthetic aeration and present potential for net autotrophic wastewater treatment in a compact system. Despite obvious differences inherent in the two, cryoconite and OPG pose striking resemblance. In both, the order Oscillatoriales in Cyanobacteria envelope inner materials and develop dense spheroidal aggregates. We explore the mechanism of photogranulation on account of high similarity between cryoconites and OPGs. We contend that there is no universal external cause for photogranulation. However, cryoconites and OPGs, as well as their intra variations, which are all are under different stress fields, are the outcome of universal physiological processes of the Oscillatoriales interfacing goldilocks interactions of stresses, which select for their manifestation as granules. Finding the rules of photogranulation may enhance engineering of glacier and wastewater systems to manipulate their ecosystem impacts.
Soil carbon cycling and ecosystem functioning can strongly depend on how microbial communities regulate their metabolism and adapt to changing environmental conditions to improve their fitness. Investing in extracellular enzymes is an important strategy for the acquisition of resources, but the principle behind the trade-offs between enzyme production and growth is not entirely clear. Here we show that the enzyme production rate per unit biomass may be regulated in order to maximize the biomass specific growth rate. Based on this optimality hypothesis, we derive mathematical expressions for the biomass specific enzyme production rate and the microbial carbon use efficiency, and verify them with experimental observations. As a result of this analysis, we also find that the optimal enzyme production rate decays hyperbolically with the soil organic carbon content. We then show that integrating the optimal extracellular enzyme production into microbial models may change considerably soil carbon projections under global warming, underscoring the need to improve parameterization of microbial processes.
Evaluation of spatially distributed crop coefficient (Kc) for estimating evapotranspiration (ETc) based on remotely sensed imagery has become an essential topic in managing the demand for agricultural water. Currently, satellite (MODIS, Landsat, etc.) imageries are not insufficient to detect variability within the small agricultural field due to its lack of desired spatial and temporal resolutions. Unmanned Aerial Vehicle (UAV) equipped with various sensors like Multispectral (MS), Thermal, and Hyperspectral cameras is becoming an emerging technology to overcome these limitations over small agricultural fields. A field experiment is carried out in the Agricultural and Food Engineering (AGFE) Department, IIT Kharagpur, to estimate Kc over the small Agri. Field using UAV-based MS cameras during Kharif (monsoon) 2019-2020 season. Lysimeters are used for estimating daily ETc for conventionally irrigated paddy crops. Reference evapotranspiration (ET0) is also calculated using the weather data of the study area. High-resolution multispectral imageries are acquired using a quad-copter UAV. The imageries are pre-processed using Pix4Dmapper software, and various vegetation indices (such as NDVI, TNDVI, NDRE, RVI, GNDVI, and LCI) are evaluated. The vegetation indices (VIs) are correlated with ground truth Kc values and spatially distributed Kc maps for the whole study area are generated based upon the excellent correlation between the VIs and ground Kc. The spatial Kc maps clearly show the variation in Kc within the plots and will be helpful for the calculation of Kc for any field without a lysimeter experiment. Generated Kc maps describe the crop water demand by visual color variations within the field. This approach may be helpful in understanding the variability in crop water requirements within the field Keywords: UAV, Crop Coefficient (Kc), Crop Evapotranspiration (ETc), Vegetation Indices, Remote Sensing.
As climate and land use increase the variability of many ecosystems, forecasts of ecological variables are needed to inform management and use of ecosystem services. In particular, forecasts of phytoplankton would be especially useful for drinking water management, as phytoplankton populations are exhibiting greater fluctuations due to human activities. While phytoplankton forecasts are increasing in number, many questions remain regarding the optimal model time step (the temporal frequency of the forecast model output), time horizon (the length of time into the future a prediction is made) for maximizing forecast performance, as well as what factors contribute to uncertainty in forecasts and their scalability among sites. To answer these questions, we developed near-term, iterative forecasts of phytoplankton 1 to 14 days into the future using forecast models with three different time steps (daily, weekly, fortnightly), that included a full uncertainty partitioning analysis at two drinking water reservoirs. We found that forecast accuracy varies with model time step and forecast horizon, and that forecast models can outperform null estimates under most conditions. Weekly and fortnightly forecasts consistently outperformed daily forecasts at 7-day and 14-day horizons, a trend which increased up to the 14-day forecast horizon. Importantly, our work suggests that forecast accuracy can be increased by matching the forecast model time step to the forecast horizon for which predictions are needed. We found that model process uncertainty was the primary source of uncertainty in our phytoplankton forecasts over the forecast period, but parameter uncertainty increased during phytoplankton blooms and when scaling the forecast model to a new site. Overall, our scalability analysis shows promising results that simple models can be transferred to produce forecasts at additional sites. Altogether, our study advances our understanding of how forecast model time step and forecast horizon influence the forecastability of phytoplankton dynamics in aquatic systems, and adds to the growing body of work regarding the predictability of ecological systems broadly.
Wetlands are endangered ecosystems that provide valuable services to society and contribute to maintaining biodiversity in low-lying areas. Hurricanes, among other stressors such as sea level rise (SLR) and anthropogenic activities, alter wetland dynamics and shape coastal morphology by redistributing sediments in estuaries and bays. Hurricane forcing plays a key role in sediment deposition and erosion within coastal wetlands and their surroundings; hence maintaining marsh elevation relative to SLR as well as eroding the edge of marsh platforms. In this study, we reconciled observed spatiotemporal patterns of wetland coverage change from multi-source remote sensing imagery with hydrodynamic simulations of both average and extreme(hurricane-like) scenarios in Mobile Bay, AL, USA. To account for sediment deposition and erosion in coastal wetlands, we constructed ‘generic’ LiDAR-derived digital elevation models (DEMs) corrected for wetland elevation errors (vertical bias) and used them as a proxy of historical DEMs. We then associated changes in wetland elevation and coverage to inundation duration and estimated the likelihood of wetlands to be either fully exposed or inundated in both scenarios. Results indicated that the likelihood of sediment deposition peaks between 4-h and 7-h of inundation for both average ande xtreme conditions. The likelihood of erosion for average conditions peaks between 11-h and 16-h, whereas that of extreme conditions is highly dependent on hurricane forcing characteristics and peaks around 6-h in the case of Hurricane Ivan (Sep/2004) and 21-hfor Hurricane Katrina (Aug/2005). Results revealed that Hurricane Ivan and Katrina had a two-sided effect on Mobile Bay’s coastal wetlands: (i) erosion along shorelines and marsh edges due to extreme coastal water levels and strong winds, and(ii) sediment deposition in landward direction due to both hurricane-induced sediment deposition and fluvial sediment input. We acknowledge that, next to hurricane forcing, an increase in sea levels could also affect sediment dynamics and so alter coastal morphology and compromise wetland survivability.
Improving nutrient and water uptake in crops is one of the major challenges to sustain a fast-growing population that faces increasingly nutrient limited soils. Root hairs, which are specialized epidermal cells, are important drivers of nutrient and water uptake from the soil. Microscopy provides a mean to record root hairs as digital images. However, due to their geometry and complex spatial arrangements quantifying root hairs in microscopy images manually remains a bottleneck. Manual selection of representative root hairs can result in inaccurate estimations of root hair traits and misrepresentation of root hair functions. We present a method to quantify phenotypes automatically by measuring all individual root hairs in digital microscopy images. Our method uses random forests classification to separate root hair from the parent root and the image background. We define metrics to evaluate segments of root hairs that intersect or form blobs of two or more root hairs. Using simulated annealing for combinatorial optimization, we reconstruct individual root hairs by resolving intersections in a globally optimal way. As a result, we measure root hair length, its distribution, and root hair density in each image. We validate our method on examples of three maize cultivars under phosphorus, nitrogen, and potassium stress. Results show that our measurements of root hair traits strongly correlate with manually measured validation data in mean root hair length (Pearson-correlation: 0.74 to 0.88, p<.001), as well as in root hair density (Pearson-correlation: 0.65 to 0.84, p<.001). We show that our method distinguishes subtle differences between genotypes and treatments based on the extracted traits and believe that our study paves a way towards identifying the genetic control of root hair traits and increased agricultural production.