Soil carbon is intimately related to the living part of the organic matter, as represented by the soil microbial biomass, which mediates the decomposition, mineralization, and immobilization of organic carbon available in soils under different land-use systems. Forest-to-agriculture conversion and land-use change often lead to a loss in microbial biomass carbon (MBC) and shifts in microbial activity, directly influencing the soil carbon dynamics. The main aim of this study was to evaluate the effects of land-use change and geographical distribution on the microbial and environmental patterns related to soil C-dynamics. We evaluated MBC and microbial respiration in soils under five different land-use systems and two contrasting seasons, at a regional scale in Santa Catarina State, Southern Brazil. At the west mesoregion, changes in the MBC were correlated to sampling season in forest and grassland systems. Yet at the plateau mesoregion, we observed a land-use effect, as MBC decreased in no-till and crop-livestock integration systems. At the two mesoregions, forest and grassland had presented the highest values of MBC and microbial activity, as represented by microbial respiration. The grassland sites have presented lower values of the metabolic quotient (qCO2) and higher values of the microbial quotient (qMic). The qCO2 was lower in winter for all land-use systems. The forest sites have shown the highest total and particulate organic carbon values. The chemical-physical characteristics have shown correlations with microbiological variables related to the soil microbial C-dynamics. The land-use intensity, season, and geographic location were the main drivers of changes in microbial C-dynamics.
Recent progress in satellite observations has provided unprecedented opportunities to monitor vegetation activity on the global scale. However, a major challenge in fully utilizing remotely sensed data to constrain land surface models (LSMs) lies in inconsistencies between simulated and observed quantities. Transpiration and gross primary productivity (GPP) that traditional LSMs simulate are not directly measurable from space and they are inferred from spaceborne observations using assumptions that are inconsistent with those of the LSMs, whereas canopy reflectance and fluorescence spectra that satellites can detect are not modeled by traditional LSMs. To bridge these quantities, we present the land model developed within the Climate Modeling Alliance (CliMA), which simulates global-scale GPP, transpiration, and hyperspectral canopy radiative transfer (RT). Thus, CliMA Land can predict any vegetation index or outgoing radiance, including solar-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near infrared reflectance of vegetation (NIRv) for any given measurement geometry. Even without parameter optimization, the modeled spatial patterns of CliMA Land GPP, SIF, NDVI, EVI, and NIRv correlate significantly with existing observational products. CliMA Land is also very useful in its high temporal resolution, e.g., providing insights into when GPP, SIF, and NIRv diverge. Based on comparisons between models and observations, we propose ways to improve future land modeling regarding data processing and model development.
Ocean worlds such as Europa and Enceladus are high priority targets in the search for past or extant life beyond Earth. Evidence of life may be preserved in samples of surface ice by processes such as deposition from active plumes or thermal convection. Terrestrial life produces unique distributions of organic molecules that translate into recognizable biosignatures. Identification and quantification of these organic compounds can be achieved by separation science such as capillary electrophoresis coupled to mass spectrometry (CE-MS). However, the data generated by such an instrument can be multiple orders of magnitude larger than what can be transmitted back to Earth during an ocean worlds mission. This requires onboard science data analysis capabilities that summarize and prioritize CE-MS observations with limited compute resources. In response, the Autonomous Capillary Electrophoresis Mass-spectra Examination (ACME) onboard science autonomy system was created for application to the Ocean Worlds Life Surveyor (OWLS) instrument suite. ACME is able to compress raw mass spectra by two to three orders of magnitude while preserving most of its scientifically relevant information content. This summarization is achieved by the extraction of raw data surrounding autonomously identified ion peaks and the detection and parameterization of unique background regions. Prioritization of the summarized observations is then enabled by providing estimates of scientific utility, the uniqueness of an observation relative to previous observations, and the presence of key target compound signatures.
Generating renewable bioenergy crops requires varietals that are suited to grow under varying environmental conditions necessitating the development and testing of a wide range of poplar (Populus) genotypes. Meanwhile, there is an increasing demand for refining the selection process of high-performing poplars. However, a cost-effective method is still needed to predict the productivity of various poplar genotypes. Photosynthetic capacity and leaf nitrogen are important growth-related physicochemical traits, but measuring them in the field and laboratory is expensive and time-consuming. Alternatively, remote sensing of hyperspectral leaf spectra may serve as a proxy to rapidly estimate these traits, which are associated with absorption, reflection, and transmission of solar radiation. To quantify photosynthetic traits, CO2 response curves were used to estimate Rubisco-limited carboxylation rate (Vcmax), maximum electron transport rate (Jmax), and triose phosphate utilization (TPU). From the same leaves measured for photosynthesis, leaf reflectance was measured with a handheld spectroradiometer. We measured a total of 105 leaf samples, including 6 taxa with 61 different poplar genotypes. For data analyses, Least Absolute Shrinkage and Selection Operator and Principal Component Analysis were used to determine the wavelengths that were the most useful for capturing the variability in the physicochemical data. Results showed that leaf reflectance at 758 nm and 936 nm were crucial wavelengths for predicting Vcmax (RMSPE = 31%) and Jmax (RMSPE = 32%), while 687 nm and 757 nm were important predictors for TPU (RMSPE = 31%), and 709 nm and 927 nm were important predictors for leaf nitrogen (RMSPE = 22%). The wavelengths near 687 nm and 760 nm are the oxygen absorption bands, and also overlap with the chlorophyll fluorescence emission of plants. Therefore, it is possible to apply hyperspectral reflectance models for rapid clonal screening and high-throughput field phenotyping of photosynthetic capacity parameters and leaf nitrogen of various poplar genotypes.
We report the interaction between non-spherical swimmers and a long-standing flow structure, Lagrangian coherent structures (LCSs), in a weakly turbulent two-dimensional flow. Using a hybrid experimental-numerical model, we show that rod-like swimmers have a much stronger and more robust preferential alignment with attracting LCSs than with repelling LCSs. Tracing the swimmers’ Lagrangian trajectories, we reveal that the preferential alignment is the consequence of the competition between the intrinsic mobility of the swimmers and the reorientation ability of the strain rate near the attracting LCSs. The strong preferential alignment with attracting LCSs further leads to a strong clustering near the attracting LCSs. Moreover, we show the self-similarity of this clustering, which reduces the intricate interaction to only one control parameter. Our results generically elucidate the interaction between active and non-spherical swimmers with LCSs and, thus, can be widely applied to many natural and engineered fluids including ocean flow.
Observations of Planet Earth from space are a critical resource for science and society. Satellite measurements represent very large investments and United States (US) agencies organize their effort to maximize the return on that investment. The US National Research Council conducts a survey of earth science and applications to prioritize observations for the coming decade. The most recent survey prioritized a visible to shortwave infrared imaging spectrometer and a multi-spectral thermal infrared imager to meet a range of needs. First, and perhaps, foremost, it will be the premier integrated observatory for observing the emerging impacts of climate change . It will characterize the diversity of plant life by resolving chemical and physiological signatures. It will address wildfire, observing pre-fire risk, fire behavior and post-fire recovery. It will inform responses to hazards and disasters guiding responses to a wide range of events, including oil spills, toxic minerals in minelands, harmful algal blooms, landslides and other geological hazards. The SBG team analyzed needed instrument characteristics (spatial, temporal and spectral resolution, measurement uncertainty) and assessed the cost, mass, power, volume, and risk of different architectures. The Research and Applications team examined available algorithms, calibration and validation and societal applications and used end-to-end modeling to assess uncertainty. The team also identified valuable opportunities for international collaboration to increase the frequency of revisit through data sharing, adding value for all partners. Analysis of the science, applications, architecture and partnerships led to a clear measurement strategy and a well-defined observing system architecture.
Improving the representation of plant hydraulic behavior in vegetation and land-surface models is critical for improving our predictions of the impacts of drought stress on ecosystem carbon and water fluxes. Species-specific hydraulic traits play an important role in determining the response of ecosystem carbon and water fluxes to water stress. Here, we present plans for the development of the Finite-difference Ecosystem-scale Tree Crown Hydrodynamics model version 3 (FETCH3), a tree hydrodynamic model which builds upon its predecessors FETCH and FETCH2. FETCH3 simulates water transport through the soil, roots, and xylem as flow through porous media. The model resolves water potentials along the vertical dimension, and stomatal response is linked to xylem water potential. The tree-level model is scaled to the plot scale based on the species composition and canopy structure of the plot, allowing the model to be validated using both tree-level observations (sap flux) and plot-level observations (eddy covariance). We will collect data from multiple sites that have both sap flux and eddy covariance measurements for analysis. The Predictive Ecosystem Analyzer (PEcAn) will be used for optimization of the hydraulic parameters in FETCH3 for different plant types in multiple sites. We plan to use this new modeling framework to examine the interactions among water stress, species-specific hydraulic strategies, and stomatal regulation across different species and ecosystem types.
1. Freshwater phytoplankton communities are currently experiencing multiple global change stressors, including increasing frequency and intensity of storms. An important mechanism by which storms affect lake and reservoir phytoplankton is by altering the water column’s thermal structure (e.g., changes to thermocline depth). However, little is known about the effects of intermittent thermocline deepening on phytoplankton community vertical distribution and composition or the consistency of phytoplankton responses to varying frequency of these disturbances over multiple years. 2. We conducted whole-ecosystem thermocline deepening manipulations in a small reservoir. We used an epilimnetic mixing system to experimentally deepen the thermocline in two summers, simulating potential responses to storms, and did not manipulate thermocline depth in two succeeding summers. We collected weekly depth profiles of water temperature, light, nutrients, and phytoplankton biomass as well as discrete samples to assess phytoplankton community composition. We then used time-series analysis and multivariate ordination to assess the effects of intermittent thermocline deepening due to both our experimental manipulations and naturally-occurring storms on phytoplankton community structure. 3. We observed inter-annual and intra-annual variability in phytoplankton community response to thermocline deepening. We found that peak phytoplankton biomass was significantly deeper in years with a higher frequency of thermocline deepening events (i.e., years with both manipulations and natural storms) due to weaker thermal stratification and deeper depth distributions of soluble reactive phosphorus. Furthermore, we found that the depth of peak phytoplankton biomass was linked to phytoplankton community composition, with certain taxa being associated with deep or shallow biomass peaks, often according to functional traits such as optimal growth temperature, mixotrophy, and low-light tolerance. 4. Our results demonstrate that abrupt thermocline deepening due to water column mixing affects both phytoplankton depth distribution and community structure via alteration of physical and chemical gradients. In addition, our work supports previous research that phytoplankton depth distribution and community composition interact at inter-annual and intra-annual timescales. 5. Variability in the inter-annual and intra-annual responses of phytoplankton to abrupt thermocline deepening indicates that antecedent conditions and the seasonal timing of surface water mixing may mediate these responses. Our findings emphasize that phytoplankton depth distributions are sensitive to global change stressors and effects on depth distributions should be taken into account when predicting phytoplankton responses to increased storms under global change.
Arctic amplification is known to accelerate the hydrological cycle in high-latitude landmass, which eventually leads to increased river discharge into the Arctic Ocean. However, the majority of climate models in Coupled Model Intercomparison Project 5 (CMIP5) tend to underestimate Arctic river discharge. This study elucidates the role of additional Arctic river discharge for the phytoplankton responses in the present and future climate simulations. In the present climate simulation, the additional freshwater input showed a decrease in the phytoplankton in spring due to the increasing sea ice, and in summer, it showed an increase in phytoplankton due to the surplus nitrate leftover from spring and induced vertical mixing. Similar processes occurred in future climate simulations. However, in those simulations, the major response region of phytoplankton to additional freshwater input was altered from the Eurasian Basin to the Canadian Basin and the East-Siberian Sea. This is because the current marginal ice zone in the Barents-Kara Sea, where phytoplankton mainly responds, moves toward the East-Siberian-Chukchi Sea. We suggest that Arctic river discharge is potentially an important contributing factor for Arctic ecosystems in both present and future climate that controls sea ice and nutrient distribution.
Plant growth is the product of gene by environment (GxE) interactions during plant development. Effective characterization of plant growth under various conditions provides insight into genetic components of plant development and mechanisms of stress resilience. While the emergence of high-through phenotyping facilities provides new avenues to further understand plant development and stress responses, the large costs of such facilities are hindering the study of dynamic growth processes. To democratize high-throughput plant phenotyping, we developed three sets of image-based phenotyping devices utilizing Raspberry Pi computers and low-weight/low-cost materials to continuously monitor shoot and root growth. The process is further automated by our workflows including data collection and statistical analysis. Our devices and workflows are customizable to image a wide variety of plants and tissues. To validate our system, we measured growth of Arabidopsis rosettes, tomato roots, and characterized the relationship between cowpea growth and evapotranspiration. These results demonstrate the variety of applications for Raspberry Pi based phenotyping. Importantly, this low-cost system is ideal for studying the genetics of plant growth and identifying new components of abiotic stress tolerance in a wide variety of species.
Imaging spectroscopy data is becoming more readily available from different satellite and airborne platforms. As this data becomes more prolific, there is a need for shared data tools and code for wrangling, cleaning, and analyzing it. The geospatial Imaging Spectroscopy Processing Environment on the Cloud (ImgSPEC) pioneers an on-demand science data processing platform with scalable back-end compute. It considers user experience and facilitates open science. ImgSPEC enables users to create data products in areas of interest using default workflows from registered algorithms, while also enabling users to customize scripts and workflows. ImgSPEC seamlessly interfaces with NASA Earthdata Search and tracks appropriate metadata for reproducibility when generating data products to share with others. Users can work in their preferred workspace (e.g., Rstudio, Jupyterlab, or command line) thereby facilitating use of open science software packages and collaborative coding through Git. ImgSPEC leverages existing NASA-funded information technologies such as the hybrid on-premise/cloud science data system (HySDS) and the Multi-mission Algorithm and Analysis Platform (MAAP). It also creates seamless interfaces with NASA-funded ECOSIS – a crowd-sourced spectral database, and ECOSML – a crowd-sourced model database. We demonstrate ImgSPEC on the Terrestrial Ecosystem use case processing through to foliar traits and fractional cover, thus aligning with driving thrusts for the NASA Surface Biology and Geology (SBG) Science and Applications Communities. As this technology is more widely adopted the interface with Amazon Web Services and NASA Earthdata search will enable broader use of more data (publicly available or loaded by the user) across more domains.
As the leading astrobiology university student society, run by students, for students, at the University of Manchester Astrobiology Society we are embarked on a mission to spread the word about astrobiology, enrich the university community by delivering high-quality events and resources, and create a global network of students and young professionals that will become the astrobiologists of tomorrow. Ever since our foundation, we have gone beyond the classic student society activities by inviting world-renowned researchers, creating a dedicated careers program, or even organizing the first student-led online congress at the university. In addition, our innovation team always ensures that we use the latest tools and technologies to keep thriving in our ever-evolving world. In this sense, we have developed an all-in-one website where people interested in astrobiology can discover, learn and connect. Furthermore, we have also built a global community using social media, with almost 3000 followers spread over 17 countries. By leading by example, we want to spark change among student societies and for them to redefine their boundaries and to achieve bigger. At AbSciCon 2022 we aim to further pursue this goal, grow our network, share expertise, and learn from others.
Near-term freshwater forecasts, defined as sub-daily to decadal future predictions of a freshwater variable with quantified uncertainty, are urgently needed to improve water quality management as freshwater ecosystems exhibit greater variability due to global change. Shifting baselines in freshwater ecosystems due to land use and climate change prevent managers from relying on historical averages for predicting future conditions, necessitating near-term forecasts to mitigate freshwater risks to human health and safety (e.g., flash floods, harmful algal blooms). To assess the current state of freshwater forecasting and identify opportunities for future progress, we synthesized freshwater forecasting papers published in the past five years. We found that freshwater forecasting is currently dominated by near-term forecasts of water quantity and that near-term water quality forecasts are fewer in number and in early stages of development (i.e., non-operational), despite their potential as important preemptive decision support tools. We contend that more freshwater quality forecasts are critically needed, and that near-term water quality forecasting is poised to make substantial advances based on examples of recent progress in forecasting methodology, workflows, and end user engagement. For example, current water quality forecasting systems can predict water temperature, dissolved oxygen, and algal bloom/toxin events five days ahead with reasonable accuracy. Continued progress in freshwater quality forecasting will be greatly accelerated by adapting tools and approaches from freshwater quantity forecasting (e.g., machine learning modeling methods). In addition, future development of effective operational freshwater quality forecasts necessitates substantive engagement of end users throughout the forecast process, funding, and training opportunities. Looking ahead, near-term forecasting provides a hopeful future for freshwater management in the face of increased variability and risk due to global change, and we encourage the freshwater scientific community to incorporate forecasting approaches in water quality research and management.
Material samples are vital across multiple scientific disciplines with samples collected for one project often proving valuable for additional studies. The Internet of Samples (iSamples) project aims to integrate large, diverse, cross-discipline sample repositories and enable access and discovery of material samples as FAIR data (Findable, Accessible, Interoperable, and Reusable). Here we report our recent progress in controlled vocabulary development and mapping. In addition to a core metadata schema to integrate SESAR, GEOME, Open Context, and Smithsonian natural history collections, three small but important controlled vocabularies (CVs) describing specimen type, material type, and sampled feature were created. The new CVs provide consistent semantics for high-level integration of existing vocabularies used in the source collections. Two methods were used to map source record properties to terms in the new CVs: Keyword-based heuristic rules were manually created where existing terminologies were similar to the new CVs, such as in records from SESAR, GEOME, and Open Context and some aspects of Smithsonian Darwin Core records. For example specimen type =liquid>aqueous in SESAR records mapped to specimen type = liquid or gas sample and material type = liquid water. A machine learning approach was applied to Smithsonian Darwin Core records to infer sampled feature terms from record text describing habitat, locality, higher geography, and higher classification fields. Applying fastText with a 600-billion-token corpus in the general domain, we provided the machine a level of “understanding” of English words. With 200 and 995-record training sets, 87%, 94% precision and 85%, 92% recall were obtained respectively, yielding performance sufficient for production use. Applying these approaches, more than 3x106 records of the four large collections have been mapped successfully to a common core data model facilitating cross-domain discovery and retrieval of the sample records.
Laboratory studies have shown that rhizodeposits could lead to either soil structural formation or dispersion depending on plant species, soil conditions, and microbial activity. However, these studies have usually been conducted in dry soils and rarely considered the combined effect of rhizodeposit and organic residues on soil structure. This study hypothesizes that root exudates promote soil dispersion initially, but over time decomposition of root exudates produce binding agents that promote stable soil structure in the rhizosphere. To test this hypothesis, a sandy loam soil sieved to < 500 µm particle size was first amended with root exudate compounds (14.4 mg C g-1), δ13C-barley residue (0.44 mg C g-1 soil), or both. Six replicate samples per treatment were packed in cores to a bulk density of 1.27 g cm-3 and then equilibrated on a tension table at -2 kPa matric potential. Rheological measurements of flow characteristics (dynamic viscosity) and strength (storage modulus, loss modulus, tan δ, and yield stress) of the control and amended soils were obtained immediately after amendment and after twelve days of incubation at 20 oC. Only root exudate compounds initially decreased the capacity of soil to retain water at -2 kPa by 21% and by 49% after incubation. Likewise, the yield stress of root exudate amended soil was significantly (P < 0.05) lower than that of the unamended soil, reflecting dispersion of soil. However, microbial decomposition/activities significantly (P < 0.05) increased yield stress over the corresponding pre-incubation values for these treatments by 200% (root exudate) and 230% (root exudate + δ13C-barley residue). These results confirmed the hypothesized dual effect of root exudates on rhizosphere structure. The initial soil dispersion may facilitate root growth by augmenting soil penetrability and releasing nutrients that were occluded in soil aggregates, whereas stable soil structure is achieved upon decomposition of root exudates.
Extremely halophilic archaea are microbes that thrive under very high salinities (>20% NaCl) and are almost exclusively placed in the class Halobacteria. In addition to their characteristic preference for high salinity and moderately high temperatures, many species of this class are resistant to desiccation, vacuum, and radiation, making them interesting targets for Astrobiological studies as model organisms and particularly relevant for the study of Mars, as highlighted by several authors. This class has a wide environmental range and includes species that live in salty biotopes such as salterns, salted foods, subterranean halite, lakes, or even in deep-sea brines in a list that includes several analogue sites. One current bottleneck of research with this group is the dispersed nature of data associated with its species. Our study partly addresses this by compiling phenotypic information and records of astrobiological experiments for all Halobacteria. We have established a database (HAPIE- Halophilic Archaea Phenotypic Information Explorer) that allows us to quickly compare different species as well as analyse trends and identify knowledge gaps and research opportunities. Our study identified gaps in coverage and knowledge (both at the level of taxonomy and range of tested parameters) and assisted us in defining new testing priorities.
Allergic respiratory disease affects millions of Americans, resulting in billions in medical expenses and lost productivity annually. Information regarding when pollen concentrations are increasing across the country is limited, diminishing the ability of health care professionals and individuals suffering from allergies and asthma to anticipate and manage their symptoms. The USA National Phenology Network, a science and monitoring network that collects, stores, and shares data and information products regarding the timing of seasonal events from across the country, offers a series of map and short-term forecast products that indicate the start of biological activity in the spring, based mainly on temperature conditions. In this study, we evaluate the potential for the Spring Indices to indicate the timing of the start and peak of airborne pollen concentrations by plant taxa, and by extension, their utility for predicting timing of increases in airborne allergenic pollen concentrations. We compared daily pollen counts collected at National Allergy Bureau (NAB) pollen counting stations across the country to the day of year the two Spring Indices – the Leaf Index and the Bloom Index – were met at those locations. In general, the Bloom Index exhibited stronger relationships with the timing of peaks in airborne pollen among the 36 plant taxa evaluated. This is likely because the Bloom Index occurs later in the season, closer to the timing of pollen peaks. However, relationships for the Leaf Index also demonstrate coherence (adj R2; ~ 0.5 +/- 0.15 [SD]). Relationships were generally strongest for Morus (mulberry), Populus (poplar), Fraxinus (ash), and Salix (willow), though the taxa best predicted by the Spring Indices varied by site. Strength of relationships did not vary appreciably across geography. Overall, the Spring Indices provide insight into seasonal pollen dynamics and have the potential to enhance springtime allergy management.
Astrobiology as a field is not well known by the public, and is a difficult topic to introduce to the those who have never heard of it before. This presentation will showcase projects and experiences from a museum setting to explore how to best bring up such a complex field of science, and how astrobiologists can make their work as digestible as possible to the general public.