Tocochromanols (vitamin E) are an essential part of the human diet. Plant products including maize grain are the major dietary source of tocochromanols; therefore, breeding maize with higher vitamin content (biofortification) could improve human nutrition. Incorporating exotic germplasm in maize breeding for trait improvement including biofortification is a promising approach and an important research topic. However, information about genomic prediction of exotic-derived lines using available training data from adapted germplasm is limited. In this study, genomic prediction was systematically investigated for nine tocochromanol traits within both an adapted (Ames Diversity Panel) and an exotic-derived (BGEM) maize population. While prediction accuracies up to 0.79 were achieved using gBLUP when predicting within each population, genomic prediction of BGEM based on an Ames Diversity Panel training set resulted in low prediction accuracies. Optimal training population (OTP) design methods FURS, MaxCD, and PAM were adapted for inbreds and, along with the methods CDmean and PEVmean, often improved prediction accuracies compared to random training sets of the same size. When applied to the combined population, OPT designs enabled successful prediction of the rest of the exotic-derived population. Our findings highlight the importance of leveraging genotype data in training set design to efficiently incorporate new exotic germplasm into a plant breeding program.
Hyperhydricity often occurs in plant tissue culture, seriously influencing the commercial micropropagation and genetic improvement. DNA methylation has been studied for its function in plant development and stress responses. However, its potential role in hyperhydricity is unknown. In this study, we report the first comparative DNA methylome analysis of normal and hyperhydric Arabidopsis seedlings using whole-genome bisulfite sequencing. We found that the global methylation level decreased in hyperhydric seedlings, and most of the differentially methylated genes were CHH hypomethylated genes. Moreover, the bisulfite sequencing results showed that hyperhydric seedlings displayed CHH demethylation patterns in the promoter of the ACS1 and ETR1 genes, resulting in up-regulated expression of both genes and increased ethylene accumulation. Furthermore, hyperhydric seedling displayed reduced stomatal aperture accompanied by decreased water loss and increased phosphorylation of aquaporins accompanied by increased water uptake. While AgNO3 prevented hyperhydricity by maintained the degree of methylation in the promoter regions of ACS1 and ETR1 and down-regulated the transcription of both genes. AgNO3 also reduced the content of ethylene together with the phosphorylation of aquaporins and water uptake. Taken together, this study suggested that DNA demethylation is a key switch that activates ethylene pathway genes to enable ethylene synthesis and signal transduction, which may subsequently influence aquaporin phosphorylation and stomatal aperture, eventually cause hyperhydricity; thus, DNA demethylation plays a crucial role in hyperhydricity. These results provide insights into the epigenetic regulation mechanism of hyperhydricity, and confirm the role of ethylene and AgNO3 in hyperhydricity control.
Marine free-living bacteria play a key role in the cycling of essential biogeochemical elements, including iron (Fe), during their uptake, transformation and release of organic matter. Similar to phytoplankton, the growth of free-living bacteria is regulated by resources such as Fe, and the low availability of these resources may influence bacterial interactions with phytoplankton, causing knock-on effects for biogeochemical cycling. Yet, knowledge of the factors limiting free-living bacterial growth and their role within the Fe cycle is poorly constrained. Here, we explicitly represent free-living bacteria in a global ocean biogeochemistry model to address these questions. We find that although Fe can emerge as proximally limiting in the tropical Pacific and in high-latitude regions during summer, the growth of free-living bacteria is ultimately controlled by the availability of labile dissolved organic carbon. In Fe-limited regions, free-living bacterial biomass is sensitive to their Fe uptake capability in seasonally Fe-limitation regions and to their minimum Fe requirements in regions perennially Fe-limited. Fe consumption by free-living bacteria is significant in the upper ocean in our model, and their competition with phytoplankton for Fe affects phytoplankton growth dynamics. The impact of free-living bacteria on the Fe distribution in the ocean interior is small due to a tight coupling between Fe uptake and release. Moving forward, future work that considers particle-attached bacteria and different bacterial metabolisms is needed to explore the broader role of bacteria in ocean Fe cycling. In this context, the global growing ’omics data from ocean observing programs can play a crucial role.
Ecosystem metabolism quantifies the rate of production, maintenance, and decay of organic matter in terrestrial and aquatic systems. It is a fundamental measure of energy flow associated with biomass production by photosynthesizing organisms and biomass oxidation by respiring plants, animals, algae, and bacteria (Bernhardt et al., 2022) . Ecosystem metabolism also provides an understanding of energy flow to higher trophic levels that supports secondary and tertiary productivity, as well as helping to explain when aquatic ecosystems undergo out-of-balance behaviors such as harmful algal blooms and hypoxia. Recent advances in sensor technology and modeling capabilities have enabled estimation of aquatic system metabolism and gas exchange over long time periods in rivers, streams, ponds, and wetlands where oxygen sensors have been deployed. Here we present RiverMET, a framework for estimation of river metabolism, with workflows to streamline data preparation, run a stream metabolism model, assess the model performance, and flag and censor final output data. The workflows are specifically tailored to use streamMetabolizer, a model for one-station calculations of stream metabolism that calculates gross primary productivity (GPP), ecosystem respiration (ER) and the air-water gas exchange rate constant (K600). We advise potential users of RiverMET to review core publications for the streamMetabolizer model (Appling et al., 2018 a, b, c) to ensure best practices that produce the most useful results. We encourage feedback about our workflows, although issues regarding the streamMetabolizer model itself should be referred to the model authors. We tested RiverMET by calculating GPP, ER, and K600 across 17 river sites in the Illinois River basin (ILRB). Each river had between one and nine years of sensor data appropriate for modeling metabolism. In total, metabolism was modeled on 15,176 days between 2005 and 2020. Overall confidence in the results was rated as high at nine river sites, medium at six river sites, and poor at two river sites. Twenty-nine percent of the total modeled days had performance metrics that triggered flags. Metrics used for daily flagging are provided with the final output, with an option to only retain the censored daily outputs with high confidence (representing 72 %, i.e., 10,938 days, of the total days modeled). This work was completed as part of the U.S. Geological Survey Proxies Project, an effort supported by the Water Mission Area (WMA) Water Quality Processes program to develop estimation methods for harmful algal blooms (HABs), per- and polyfluoroalkyl substances (PFAS), and metals, at multiple spatial and temporal scales.
Chaotropicity (order-destroying) describes the entropic disordering of lipid bilayers and other biomacromolecules which is caused by substances dissolved in water. Solvents in water are defined as kosmotropic (order-making) if they contribute to the stability and structure of water-water interactions. These interactions between brine solutions (water and salt) and ancestral proteins (AncC ribonuclease) induce varying changes in the protein’s structure. Understanding how these brine solutions and early protein structures interact provides insight into the origins of life and zones of habitability across the solar system. Here, we used a molecular dynamics simulator to assess the reaction of an ancient protein (ribonuclease sequence) when exposed to .15M and 1.5M concentrations of MgCl2 and NaCl. The ancient ribonuclease structure responded uniquely to .15M NaCl and both concentrations of MgCl2. Both the nature of the cation and concentration of the salt promote different responses and effects in the secondary structures of the AncC protein. According to the Hoffmeister Series scale, sodium is more kosmotropic and magnesium is more chaotropic. These two different salts with two different chao-kosmo properties create two different responses within the protein structure in that particular brine. This observation speaks highly to the significance of chao-kosmo influences on molecular level outcomes.
Human activities are driving environmental and climatic changes, affecting the distribution and diversity of species worldwide. Limiting the negative impacts of these activities on wildlife requirestimely knowledge of status and trends in populations over large scales. Camera trapping providesopportunities to simultaneously collect information on several species over large spatio-temporalscales. However, the time required to process large collections of images, the statistical andprogrammatic skills needed to analyze large sets of data, and a general lack of homogeneity in metadata standards hinder the use of camera trapping for local and global conservation. WildlifeInsights (http://wildlifeinsights.org/; WI) is a web platform that promotes and supports the use andsharing of camera-trap data for species conservation and promotes the mobilization of records thatotherwise might be permanently siloed in private data-storage units or lost over time. WI speeds upthe processing of images via an AI model trained to classify >700 species, and automates commonstatistical analysis through a standardized, accessible user interface. It also provides tools to addresscommon issues faced by camera trappers, such as the need of hiding locations of sensitive speciesand removing images of humans, and has a transparent infrastructure to request, share and citedatasets. Although only recently open to the public, the platform already hosts tens of millionsrecords, most of which publicly accessible, from more than 50 countries and 1000 species. Using datashared in WI, we assessed whether information collected using camera traps improved the spatial,temporal, taxonomical, and ecological coverage of many species compared to records available inmore traditional open-access repositories such as GBIF. Birds and mammals, and countries with ahigh proportion of remote areas and biodiversity had the largest increases in coverage. Compared toother traditional methods, camera traps also provided fi ner-resolution temporal information, oftenreplicated over time. Our results showed the importance of sharing camera-trap data for conservationand highlights WI’s role as an invaluable resource to timely inform biodiversity conservation in achanging world
Mosquitoes in recent years have increased greatly in numbers due to the rapidly changing climate and rising temperatures. With this change comes suitable habitats for mosquitoes which are the most efficient killers in all of the animal kingdom due to the number of death from mosquito-borne diseases. If we were able to pinpoint the certain areas that mosquitoes are most attracted to we could in theory slow or even prevent the spread of mosquitoes. In our project, the research was conducted to find a correlation between the color and size of the traps to the amount of mosquitos that are present. With our findings, we were able to conclude that the bigger the traps, the faster the mosquitoes would be attracted to that area. We also found that the different traps would hold similar densities of mosquito larvae per square inch.
The importance of animals within fluvial geomorphology (zoogeomorphology) is increasingly recognized. Caddisflies (Trichoptera) are a group of aquatic insects known for their bioconstructions. Many caddisfly construct cases from fine sediment and silk. Caddisfly cases differ in size, shape and density from the incorporated sediment and case construction may therefore affect the mobility of these sediments in rivers. However, even though communities of caddisfly often use substantial quantities of sediment in case construction, the effect of these bioconstructions on sediment transport in rivers is unknown. We use a flume experiment to compare the bed shear stress required to transport (1) empty caddisfly cases and (2) individual sediment particles following disaggregation from the case. The cases of three species were considered; two that construct different styles of tubular case (Potamophlax latipennis and Sericostoma personatum) and one that builds a domed case (Agapetus fuscipes). P. latipennis and S. personatum cases were easier to entrain than the sediment grains incorporated into them, whilst A. fuscipes cases were not. Despite their low mass, A. fuscipes cases required the most shear stress to transport them because their domed shape impeded rolling. These findings are important for understanding how caddisfly affect sediment mobility in rivers and how differences in case design reflect case function to the larvae. These results suggest that un-attached tubular caddisfly cases may be preferentially transported over other particles on the river bed and thus caddisfly may increase fluvial entrainment of sand where they occur in high abundance.
The necessity to understand the influence of global ocean change on biota has exposed wide-ranging gaps in our knowledge of the fundamental principles that underpin marine life. Concurrently, physiological research has stagnated, in part driven by the advent and rapid evolution of molecular biological techniques, such that they now influence all lines of enquiry in biological and microbial oceanography. This dominance has led to an implicit assumption that physiology is outmoded, and advocacy that ecological and biogeochemical models can be directly informed by omics. However, the main modelling currencies continue to be biological rates and biogeochemical fluxes. Here we ask: how do we translate the wealth of information on physiological potential from omics-based studies to quantifiable physiological rates and, ultimately, to biogeochemical fluxes? Based on the trajectory of the state-of-the-art in biomedical sciences, along with case-studies from ocean sciences, we conclude that it is unlikely that omics can provide such rates in the coming decade. Thus, while physiological rates will continue to be central to providing projections of global change biology, we must revisit the metrics we rely upon. We advocate for the co-design of a new generation of rate measurements that better link the benefits of omics and physiology.
We elucidated the effects of snow and remineralization processes on nutrient distributions in multi-year landfast sea ice (fast ice) in Lützow-Holm Bay, East Antarctica. Based on sea-ice salinity, oxygen isotopic ratios, and thin section analyses, we found that the multi-year fast ice grew upward due to the year-by-year accumulation of snow. Compared to ice of seawater origin, nutrient concentrations in shallow fast ice were low due to replacement by clean and fresh snow. In deeper ice of seawater origin (the lower half of the multi-year fast ice column), remineralization was dominated by the degradation of organic matter. In addition, denitrification was detected in the low brine volume fraction and impermeable multi-year ice due to their disconnection from gas and water exchanges with the atmosphere and under-ice water. By comparison with first-year ice, we develop a conceptual image of the evolution of nutrient concentrations, with biological uptake dominating in relatively young ice and remineralization dominating in older, multi-year ice under the physical process of brine drainage.
In a recent study, Sosa-Gutierrez et al. (2022, https://doi.org/10.1029/2021GL097484) evaluated the potential impacts of tropical cyclones (TCs) on the Atlantic pelagic Sargassum using satellite-based Sargassum maps, 86 hurricane tracks during 2011 – 2020, and statistical analysis. The results showed an average drop of 40% in Sargassum coverage under TC trajectories, attributed to Sargassum sinking. However, there appear two issues: 1) the Sargassum maps contain large uncertainties due to methodology used in developing the maps. The impacts of these uncertainties on change detection are largely unknown, especially along the TC trajectories where cloud cover prevails; 2) there is a lack of a “control” experiment in the logic to infer causality. Based on these observations and arguments, while it is possible that TCs may have significant impacts, either positively or negatively, on pelagic Sargassum, a revisit appears necessary to use improved Sargassum maps and better experimental design before drawing conclusions.
Terrestrial soil organic carbon (SOC) dynamics play an important but uncertain role in the global carbon (C) cycle. Current modeling efforts to quantify SOC dynamics in response to global environmental changes do not accurately represent the size, distribution and flux of C from the soil. Here, we modified the Daily Century (DAYCENT) biogeochemical model by parameterizing conceptual SOC pools with C fraction data, followed by historical and future simulations of SOC dynamics. Results showed that simulations using modified DAYCENT (DCmod) led to better initialization of SOC stocks and distribution compared to default DAYCENT (DCdef) at long-term research sites. Regional simulation using DCmod demonstrated higher SOC stocks for both croplands (34.86 vs 26.17 MgC ha-1) and grasslands (54.05 vs 40.82 MgC ha-1) compared to DCdef for the contemporary period (2001-2005 average), which better matched observationally constrained data-driven maps of current SOC distributions. Projection of SOC dynamics to land cover change (IPCC AR4 A2 scenario) under IPCC AR5 RCP8.5 climate scenario showed absolute SOC loss of 8.44 and 10.43 MgC ha-1 for grasslands and croplands, respectively, using DCmod whereas, SOC losses were 6.55 and 7.85 MgC ha-1 for grasslands and croplands, respectively, using DCdef. The projected SOC loss using DCmod was 33% and 29% higher for croplands and grasslands compared to DCdef. Our modeling study demonstrates that initializing SOC pools with C fraction data led to more accurate representation of SOC stocks and individual carbon pool, resulting in larger absolute and relative SOC losses due to agricultural intensification in the warming climate.
High-resolution space-based spectral imaging of the Earth’s surface delivers critical information for monitoring changes in the Earth system as well as resource management and utilization. Orbiting spectrometers are built according to multiple design parameters, including ground sampling distance (GSD), spectral resolution, temporal resolution, and signal-to-noise. The different applications drive divergent instrument designs, so optimization for wide-reaching missions is complex. The Surface Biology and Geology component of NASA’s Earth System Observatory addresses science questions and meets applications needs across diverse fields, including terrestrial and aquatic ecosystems, natural disasters, and the cryosphere. The algorithms required to generate the geophysical variables from the observed spectral imagery each have their own inherent dependencies and sensitivities, and weighting these objectively is challenging. Here, we introduce intrinsic dimensionality (ID), a measure of information content, as an applications-agnostic, data-driven metric to quantify performance sensitivity to various design parameters. ID is computed through the analysis of the eigenvalues of the image covariance matrix, and can be thought of as the number of significant principal components. This metric is extremely powerful for quantifying the information content in high-dimensional data, such as spectrally resolved radiances and their changes over space and time. We find that the intrinsic dimensionality decreases for coarser GSD, decreased spectral resolution and range, less frequent acquisitions, and lower signal-to-noise levels. This decrease in information content has implications for all derived products. Intrinsic dimensionality is simple to compute, providing a single quantitative standard to evaluate combinations of design parameters, irrespective of higher-level algorithms, products, applications, or disciplines.
From the word go till nowadays, laboratory biologists have been focused mostly on a few practical biological models, which largely determined and narrowed down our modern vision of diverse complex physiological cellular and molecular bioprocesses. The choice of model organism is an important issue in experimental biology, particularly, in space exploration biomedical science. Largely unknown non-classical aquatic model organisms will be advantageous for further developments on the path of humanity into space. Selected perspective hydrobiont models based on old almost-forgotten and new literature data are discussed that could be of future use in the expanding biomedical space science.
Sanbon Chaka Gosa1, Bogale Abebe Gebeyo12, Ravitejas Patil1, Ramón Mencia1, Dani Zamir1, Menachem Moshelion1# 1 The R.H. Smith Institute of Plant Sciences and Genetics in Agriculture, The R.H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, 76100 Israel 2 Current address: Department of Horticulture, College of Agriculture and Natural Resource, Dilla University, Dilla, Ethiopia #Corresponding Author Abstract Plant productivity in general and under stress, in particular, is a complex and comlitative trait largely influenced by environmental conditions. Among the most important traits are the whole-plant water-balance regulation mechanisms that dynamically change in order to maximize the metabolic activity of the plant. Due to the difficulty of high-throughput phenotyping of these physiological traits (e.g. transpiration, stomatal conductance, and photosynthesis), they are usually measured in static conditions or modeled based on only a few measuring points (low resolution). To overcome this challenge, we utilized a high-throughput gravimetric functional-phenotyping platform (PlantArray) along with a practical reverse phenotyping approach. We selected 30 tomato lines from multiple years of field yield data and functionally phenotyped them for their dynamic response curves using a variety of stress scenarios implemented using drought conditions (each plant received irrigation based on the amount of water it transpired). Our results show that resilient and tolerable traits, in the field, are associated with stomatal plastic conductance, i.e., maximum under well-irrigation, yet the rapid response to changes in environmental conditions (soil and atmospheric). The plastic traits of the idiotype lines were shown to increase water use efficiency (momentarily), thus maximizing yield in water deficit conditions. Based on manual characterizations of the idiotypes, it has been found that their abaxial surfaces have a greater density of stomata and a higher aperture during the early morning. Additionally, these lines showed rapid recovery after a drought. Our study concluded that reverse functional phenotyping can significantly reduce the pre-breading processes for yield-related traits. Keywords: Functional phenotyping, crops yield, —dynamic response, drought stress, stomatal conductance, reverse phenomics
In light of the magnitude and pace of the environmental changes in the northern permafrost zone (NPZ) and their feedbacks to climate, contemporary, accurate and quantitative ecological forecasting has never been so paramount to the development of climate change adaptation and mitigation strategies. Yet, uncertainties associated with carbon (C) projections in the NPZ remain the largest to projections of global C budget and climate. While there are persisting lacks of data documenting important and emerging soil and vegetation dynamics in the NPZ, the volume, variety and accessibility of observational data in the NPZ has grown exponentially over the past decades and significantly improved our understanding of terrestrial C dynamic. Yet, a lag persists between large availability of historical, new and iterative data collections and the capacity of terrestrial biosphere models to fully incorporate this information, limiting advances in reducing the uncertainty of ecological forecasting in the NPZ. In this new project, we are developing the Arctic Carbon Monitoring and Prediction System (ACMPS), a data assimilation system that will use the information from field observations from ecological networks, remote sensing data and ecological modeling to reduce the uncertainty of the terrestrial carbon balance in the NPZ. The ACMPS will be coupling model development and testing, data-assimilation techniques and near-term forecasting capacity to improve the accuracy of historical and future simulations of ecosystem permafrost and C dynamics across the NPZ. We will present the structure and workflow of the ACMPS, as well as preliminary assessment of model sensitivity and uncertainty analysis of soil and vegetation carbon fluxes, using a terrestrial biosphere model specifically developed to represent permafrost, vegetation and carbon dynamics in arctic and boreal ecosystems. Plain-language Summary We are presenting the Arctic Carbon Monitoring and Prediction System, a data assimilation system that uses field observation, remote sensing data and ecological modeling to reduce the uncertainty of the terrestrial carbon balance in the northern permafrost zone, and to better inform development of climate change adaptation and mitigation strategies.
Continuous collection and analysis of high-resolution phenotype data is critical to develop crops resilient to the consequences of climate change. Though web-accessible tools for parallel, reproducible scientiSic workSlows render big data increasingly tractable, software for plant science remains inadequate for large-scale precision agriculture. Cyberinfrastructure must present minimal barriers to entry, accommodate rapidly changing dependencies, support a wide variety of use cases, and weave together sensors at the edge, laptops, clusters, and cloud storage into a coherent virtual workspace. PlantIT is a web portal intended as such an environment. Platforms like PlantIT and its precursor DIRT  permit efSicient phenotyping and equip geographically distributed researchers with a code-optional interface. WorkSlows are published in Docker images, deployed as Singularity containers to public or private computing resources, and monitored in real time. Data are stored automatically in the CyVerse Data Store and can be annotated according to the MIAPPE  standard. GitHub integration provides versioning and repositories can be activated with a single conSiguration Sile, like Travis or GitHub Actions. Containers allow for a range of use cases, including image-based trait measurements, 3D reconstructions, morphological growth simulations, and crop modeling. Pseudo-batch/stream processing is also necessary; as data scales, manual batch jobs rapidly become infeasible, and (re-)analysis must occur upon arrival in near-real-time. We suggest web-accessible phenotyping automation software may address bottlenecks and help reveal undiscovered relationships between genes, traits, and the environment.
Cover crops may influence soil health and functioning. However, little is known about the role of belowground root architectural traits in linking cover crop diversity with rhizosphere soil ecosystem properties. We hypothesize that cover crop diversity may improve root traits, which in return, could influence its effects on essential indicators of soil physicochemical heterogeneity, such as the composition of soil aggregate-size classes and nutrients, the soil organic matter (SOM) and soil organic carbon (SOC) contents, and microbial communities. We studied the impact of cover plant diversity on root traits, soil properties and microbial communities. The four soil aggregate-size classes, such as large macro- (>2000μm), small macro- (<2000-500μm), meso- (<500-250 μm), and micro-aggregates (<250 μm) were separated by the dry sieving. Root traits such as surface area (cm2) and length (cm) were quantified by image analysis using Winrhizo. The soil nutrient, SOM, and SOC contents were determined by standard methods. Plant diversity improved productivity, root architectural traits, composition of soil aggregate-size classes and nutrients, SOM and SOC contents, composition and networking of microbial communities. Our results suggest that competition among plant roots in species-rich than poor communities may improve rhizosphere soil carbon storage, composition of soil aggregate-size classes and microbial communities.