Soil biogeochemical models (SBMs) simulate element transfer processes between organic soil pools. These models can be used to specify falsifiable quantitative assertions about soil system dynamics and their responses to global surface temperature warming. To determine whether SBMs are useful for representing and forecasting data-generating processes in soils, it is important to conduct data assimilation and fitting of SBMs conditioned on soil pool and flux measurements to validate model predictive accuracy. SBM data assimilation has previously been carried out in approaches ranging from visual qualitative tuning of model output against data to more statistically rigorous Bayesian inferences that estimate posterior parameter distributions with Markov chain Monte Carlo (MCMC) methods. MCMC inference is better able to account for data and parameter uncertainty, but the computational inefficiency of MCMC methods limits their ability to scale assimilations to larger data sets. With formulation of efficient and statistically rigorous SBM inference frameworks remaining an open problem, we demonstrate the novel application of a variational inference framework that uses a method called normalizing flows to approximate SBMs that have been discretized into state space models. We fit the approximated SBMs to synthetic data sourced from known data-generating processes to identify discrepancies between the inference results and true parameter values and ensure functionality of our method. Our approach trades estimation accuracy for algorithmic efficiency gains that make SBM data assimilation more tractable and achievable under computational time and resource limitations.
Agricultural water resources are threatened by climatic variability and increased competition for available freshwater resources. In order to mitigate the effect of climate change on cotton production, breeders are increasing their efforts on improving drought tolerance in this essential fiber crop. To achieve this, effective screening of diverse germplasm is needed to identify useful genetic variation that can be utilized for crop improvement. Within the last decade, unmanned aerial vehicles (UAVs) have led to the ability to quickly and reliably image large areas while simultaneously decreasing temporal effects associated with a large time window for data collection. This technology allows researchers to scale their phenotyping efforts, enabling studies that utilize mapping and monitoring efforts such as plant water stress detection. In this study, we used UAV-based thermal imagery to screen a diverse population of over 350 different genotypes of cotton in order to locate varieties that exhibit cooler canopies. This diversity panel was grown under two contrasting levels of irrigation, well-watered and water-limited, with data collection flights occurring weekly for three months during the season. The thermal images were clipped to plot boundaries, soil and plant pixels were segmented, and average temperatures were extracted to identify potential drought tolerant varieties. The objectives of this study were to (i) demonstrate that UAV-based thermal imagery, along with our calibration methods, can be used to render accurate plant canopy temperature values and (ii) identify cotton genotypes that outperform others in a drought-stressed environment.
Stomata, microscopic pores on leaf surfaces, regulate the uptake of carbon dioxide and the simultaneous loss of water vapor by leaves. New image acquisition and analysis methods are allowing high-throughput phenotyping of stomatal patterning, which in turn have been applied to better understand the genetic basis of variation in certain species. However, it takes considerable data and effort to train the models, and their ability to accurately detect epidermal structures is constrained to morphologies found within the training data. This issue of context dependency, the inability to perform effectively in novel contexts, is the main hurdle preventing widespread adoption of machine learning in high-throughput phenotyping of intraspecific, interspecific, and environmental variation. Here we show the limited ability of a Mask-RCNN tool, which was previously trained and successfully applied to Zea mays, to analyze images from a closely related grass, Setaria viridis. We then demonstrate successful retraining of the tool to cope with the novel diversity presented by this new species. The stomatal complexes in optical tomography images of mature Setaria leaves were accurately identified by comparison to expert raters (R2 = 0.84). This study highlights the challenge of context dependency for widespread application of machine learning tools for phenotyping plant traits, even in closely related species. At the same time, it also provides a new tool that can be applied to leverage Setaria as a model C4 species, while also providing a roadmap for translation of a machine learning to analyze stomatal patterning in new plant species.
Rainfall runoff and leaching are the main driving forces that nitrogen, an important non-point source (NPS) pollutant, enters streams, lakes and groundwater. Hydrological processes thus play a pivotal role in NPS pollutant transport. However, existing environmental models often use oversimplified hydrological components and do not properly account for overland flow process. To better track the pollutant transport at a watershed scale, a new model is presented by integrating nitrogen-related processes into a comprehensive hydrological model, the Distributed Hydrology Soil and Vegetation Model (DHSVM). This new model, called DHSVM-N, features a nitrate transport process at a fine resolution, incorporates landscape connectivity, and enables proper investigations of the interactions between hydrological and biogeochemical processes. Results from the new model are compared with those based on Soil & Water Assessment Tool (SWAT). The new model is shown capable of capturing the “hot spots” and spatial distribution patterns of denitrification, reflecting the important role in which heterogeneity of the watershed characteristics plays. In addition, a set of control experiments are designed using DHSVM-N and its variant to study the respective role of hydrology and nitrate transport process in modeling the denitrification process. Our results highlight the importance of adequately representing hydrological processes in modeling denitrification. Results also manifest the importance of having a good transport model with accurate flow pathways that considers realistic landscape connectivity and topology in identifying the denitrification hot spots and in properly estimating the amount of nitrate removed by denitrification.
The symbiosis between crops and arbuscular mycorrhizal fungi (AMF) have become an attractive route towards achieving carbon neutral agriculture and reducing the use of chemical fertilizers. Yet, our understanding of how active AMF infections influence the uptake, allocation, and exchange of carbon is limited. Here, we combine X-ray CT and PET imaging to observe and quantify the flow of carbon from leaves to roots to hyphae. Comparison of maize grown with and without AMF allows us to measure changes in the amount of 11CO2 taken up in leaves and subsequently the amount of 11C allocated to below-ground roots. Then, co-registered CT and PET images are used to identify hot spots which may indicate active AMF infection sites. Finally, analysis of 11C kinetics at these hot spots are used to assess the amount of carbon exchanged between maize roots and hyphae. By combining structural and biochemical information, we begin to deepen our understanding of the different types of changes in carbon flow in Maize-AMF systems and how we can improve sustainable agriculture efforts.
Large rivers can retain a substantial amount of nitrogen (N), particularly in submerged aquatic vegetation (SAV) meadows that may act as disproportionate control points for N retention in rivers. However, the temporal variation of N retention remains unknown since past measurements were snapshots in time. Using high frequency measurements over the summers 2012-2017, we investigated how climate variation influenced N retention in a SAV meadow at the confluence zone of two agricultural tributaries entering the St. Lawrence River. Distinctive combinations of water temperature and level were recorded between years, ranging from extreme hot-low (2012) and cold-high (2017) summers (2 ˚C and 1.4 m interannual range). Using an indicator of SAV biomass, we found that these extreme hot-low and cold-high years had reduced biomass compared to hot summers with intermediate levels. In addition, change in main stem water levels were asynchronous with the tributary discharges that controlled NO3- inputs at the confluence. We estimated daily N uptake rates from a moored NO3- sensor, and partitioned these into assimilatory and dissimilatory pathways. Measured rates were variable but among the highest reported in rivers (median 576 mg N m-2 d-1;, range 60 – 3893 mg N m-2 d-1) and SAV biomass promoted greater proportional retention and permanent N loss through denitrification. We estimated that the SAV meadow could retain up to 0.8 kt N per year and 87% of N inputs, but this valuable ecosystem service is contingent on how climate variations modulate both N loads and SAV biomass.
Biogeochemical cycling in permafrost-affected ecosystems remains associated with large uncertainties, which could impact the Earth’s greenhouse gas budget and future climate mitigation policies. In particular, increased nutrient availability following permafrost thaw could perturb biogeochemical cycling in permafrost systems, an effect largely unexplored in global assessments. In this study, we enhance the terrestrial ecosystem model QUINCY, which fully couples carbon (C), nitrogen (N) and phosphorus (P) cycles in vegetation and soil, with processes relevant in high latitudes (e.g., soil freezing and snow dynamics). We use this enhanced model to investigate impacts of increased carbon and nutrient availability from permafrost thawing in comparison to other climate-induced effects and CO2 fertilization over 1960 to 2019 over a multitude of tundra sites. Our simulation results suggest that vegetation growth in high latitudes is acutely N-limited at our case study sites. Despite this, enhanced availability of nutrients in the deep active layer following permafrost thaw, simulated to be around 0.1 m on average since the 1960s, accounts for only 11 % of the total GPP increase averaged over all sites. Our analysis suggests that the decoupling of the timing of peak vegetative growth (week 27-29 of the year, corresponding to mid-to-late July) and maximum thaw depth (week 34-37, corresponding to mid-to-late August), lead to an incomplete plant use of newly available nutrients at the permafrost front. Due to resulting increased availability of N at the permafrost table, as well as alternating water saturation levels, increases in both nitrification and denitrification enhance N2O emissions in the simulations. Our model thus suggests a weak (5 mg N m-2 yr-1) but increasing source of N2O, which reaches trends of up to +1 mg N m-2 yr-1 per decade, locally, which is potentially of large importance for the global N2O budget.
Ocean governance is characterised by social-ecological complexity and divergence in stakeholder values and perspectives. Meeting the challenges set out in the UN Decade of Ocean Science for Sustainable Development will require transdisciplinary approaches that can embrace multiple ways of knowing to develop shared understandings within interdependent communities of practice and ensure they can be applied in interventions that are adaptive, proactive, socially just, critically reflexive and fit to meet the Decade’s challenges. We present the outcomes of an innovative participatory art process, the Exquisite Corpse Project, with the aim of highlighting multiple perspectives, and developing empathy between participants. We will engage a selected group of researchers from the emerging ‘Ocean Art-Ocean Science’ community to explore the topic of marine heatwaves and their impacts based on data collected in the Northeast Pacific by Ocean Networks Canada and other sources. Through a facilitated process, participants will create three pieces of art that will build on each other and will be exchanged between participants. At the end, all created artworks will be reviewed by the full group to explore emerging insights on marine heatwaves and to surface participants’ underlying values and emotions, which is rarely done in scientific circles where the main mode of discourse employs rational dispassionate exchange. By creating a fun, emotionally-engaging process, we aim to show how the Exquisite Corpse project can strengthen interpersonal bonds, build social cohesion, create opportunities to surface people’s values and perspectives, and develop new transdisciplinary insights in a non-confrontational way. This study is part of an ongoing process exploring transdisciplinary approaches for multidirectional art-science collaborations and developing new research methods for including artistic insight and expression within the scientific discovery process. Instead of the conventional ‘outward looking’ strategy of many art-science projects translating scientific outputs to new formats, our approach is primarily ‘inward looking’. We aim to provide an opportunity for scientists to create art, thus allowing them to explore their own emotions, values and experiences through different ways of knowing.
The search for evidence of existing or extant Life (biosignatures) is a growing research topic and one of the main pillars of Astrobiology. There is significant interest in the search and exploration of new biosignatures, and increasing relevance of Potential Biosignatures. These are specific features that although consistent with biological processes can also be attributed to inanimate processes. Biogenic Metallic nanoparticles (MNPs), have been intensively studied and explored, yet their synthesis is not yet fully understood. Despite the lack of a systematic survey on this topic, it is well known that many microbes produce molecules with the capability of reducing metal ions. Given the wide diversity of such molecules, we can assume that all microbial life is capable of synthesizing them and, consequently, producing MNPs. Researchers agree that any existing or extant life on Mars or on other parts of the solar system, is (or was) likely microbial. Therefore, the detection of MNPs formation, when analyzing extraterrestrial samples (e.g., sediments, rocks), could be used to infer the presence of biological molecules and thus be employed as a new potential biosignature. Therefore, in short: yes, biogenic metallic nanoparticles have a great potential of being used as biosignatures.
Though fascinating and multi-disciplinary in scientific horizon, Astrobiology, till date has not managed to make a commendable mark in Indian academia which raises the need of not reformation but transformation in the education system. As per our recent survey, 30.88% of 2455 participants claimed to have heard about Astrobiology for the first time, 47.01% reported to have scant knowledge, and 22.12% were familiar. In addition, the data suggests that more than 77% of enthusiasts have no access to proper guidance and resources to pursue a career in Astrobiology. Hence, to tackle such issues, Spaceonova conducted free webinars and two day workshops in collaboration with 13 renowned institutions in India like IIST, DU, VIT etc., that impacted 3869 students across 700+ unique colleges. Such an initiative introduced them to the various career opportunities in the field of Astrobiology using Bioinformatics tools like Artemis and RasMol to carry out independent in-silico analysis. To carry the momentum forward, Spaceonova seeks to collaborate with various organisations to introduce research driven Astrobiology clubs, training programmes and diplomas in India to create an Astrobiology ecosystem, where limit tends to infinity. Here, we have discussed the required methodologies and blueprint to execute the same.
Continuous observations from geostationary satellites have been utilized to understand land surface seasonal dynamics and fill data gaps caused by clouds. However, the limited spatial resolution of geostationary satellite products constrained the processes of detecting the terrestrial changes in landscape scale, particularly over heterogeneous areas. Moreover, the variation of sun-target-sensor geometry in geostationary satellites results in diurnal changes in surface reflectance products. To overcome the limitations of geostationary satellite products over heterogeneous areas, we conducted the series of processes: 1) characterizing the effect of the solar and viewing geometry in surface reflectance using the bidirectional reflectance distribution function (BRDF), 2) harmonizing the satellite products from different platforms into a seamless product, and 3) fusing the different satellite products to enhance the spatial resolution of geostationary satellite products. Finally, we adopted spatial and temporal gap-filling methods to achieve daily gap-filled surface reflectance products. For robust application, the results from the integrated process were evaluated in both space and time using hyperspectral maps derived from unmanned aerial vehicle (UAV) and in situ tower-based continuous spectral measurements. We expect the geostationary satellite products with high spatial resolution to uncover the cloud-bound areas towards sensing the changes of the Earth in space and time.
With the increasing volume of data generated in agriculture, developing guidelines for appropriate data sharing and management is essential. Founded in 2015, the AgBioData Consortium aims to identify the current major issues in data curation and management and ensure more Findable, Accessible, Interoperable, and Reusable (FAIR) data. In 2021 we received funding from the National Science Foundation for a Research Coordination Network grant, whose main goals are: (1) make recommendations and implementation plans for FAIR data in agriculture; (2) expand the AgBioData network by recruiting key stakeholders in agricultural research; (3) provide educational material to train researchers on FAIR data sharing; and (4) develop a roadmap for a sustainable genomic, genetic and breeding (GGB) Database Ecosystem. We have started by launching nine working groups covering different aspects of data management in agriculture and recruitment. We also plan new groups focused on sharing and archiving new types of data, such as those generated with high-throughput phenotyping platforms. As plant phenomics becomes a major part of agriculture, we foresee opportunities to collectively identify best data management practices in current and future phenomics repositories to ensure that phenomics data is FAIR from the start. We are welcoming new members with expertise in the field. If you are interested in joining, please visit our website (https://www.agbiodata.org).
Genomic selection (GS) can improve the efficiency of tea breeding compared to phenotypic selection (PS) by shortening the generation interval, increasing selection accuracy, and shortening the duration of the entire breeding program, especially at early stages. Tea (Camellia sinensis (L.) O. Kuntze) is mainly grown in low- to middle-income countries (LMIC) and is a global commodity. Breeding programs in these countries face the challenge of increasing genetic gain because the accuracy of selecting superior genotypes is low and resources are limited. Recurrent phenotypic selection has traditionally been the primary method for developing improved tea varieties and can take over 16 years. Therefore, the main objective of this study was to investigate the potential of implementing GS in tea breeding programs to speed up genetic progress despite the low labour costs in LMIC. We used stochastic simulations to compare three GS breeding programs with a commercial PS program over a 40-year breeding period. All GS breeding programs achieved higher genetic gains compared to PS. Seed-GSconst, in particular, proved to be the most cost-effective strategy for introducing GS into tea breeding programs. It introduces GS at the nursery stage, thereby increasing the predictive accuracy at the early stage of the breeding program. It also shortens the duration of the entire breeding program by three years and reduces the generation interval to two years. Our results indicate that GS is a promising strategy to improve genetic gain per unit time and cost in tea breeding programs.
Biological dinitrogen (N2) fixation is an important new nitrogen source in oligotrophic subtropical oceans. In numerical model studies, the east-west gradient of iron deposition as atmospheric Asian dust strongly affects the zonal distribution of N2 fixation activity in the North Pacific, but the in-situ relationship at a basin-scale is not well examined. We examined the trans-Pacific longitudinal variation in N2 fixation activity from 120°W to 137°E at 23°N in summer with environmental parameters that potentially influence diazotrophy. The dissolved inorganic iron concentration in surface water was consistently low (<0.4 nM) throughout the study area. The modelled deposition flux of iron as atmospheric dust (dust-Fe) largely increased westward, whereas labile phosphorus (phosphate and labile phosphoric monoesters) in the surface water decreased westward. N2 fixation varied between 34.6–298 µmol N m-2 day-1 and was high (>200 µmol m-2 day-1) in the central area (150–180°W), where both dust-Fe input and the phosphorus stock were in intermediate ranges. The rates of N2 fixation showed an increasing trend with dust-Fe input in the eastern and western parts of 180°, indicating that increasing dust input enhanced N2 fixation activity. However, compared with that of the eastern region, the effect of enhancement on activity was smaller in the western region, where phosphate concentration in the euphotic zone was low (<50 nM), presumably due to the higher iron requirement to utilize organic phosphorus. Our data show that phosphorus availability substantially controls the longitudinal distribution of N2 fixation through co-limitation with iron in the subtropical North Pacific.
Mosquitoes are dangerous vector organisms that spread diseases to millions of people worldwide, causing nearly one million deaths each year. This study serves to identify larvae of the malaria-spreading Anopheles genus of mosquitoes in North America while fueling methods to refine or “clean” the NASA GLOBE Observer data set and promoting Citizen Science. The GLOBE Observer app has facilitated mosquito research, allowing Citizen Scientists to report mosquito breeding grounds and the presence of larvae. In conjunction with other data, such as landcover or water, mosquito activity can be tracked and their effects can be mitigated. Citizen Science is often considered highly inaccurate, with the reasoning that almost anyone, trained expert or not, can contribute to data collection with varying levels of precision. To improve the accuracy and credibility of such Citizen Science data sets–in this case, the GLOBE Observer database–a sample of 155 unique mosquito observations were pulled from the database. Using this sample, trained classifiers and mosquito experts reclassified each reported observation to gauge Citizen Scientists’ accuracy in identifying Anopheles larvae. Using this reclassified data set, a convolutional neural network (CNN) was created as a machine learning (ML) solution to automatically identify a given larva photo as Anopheles or non-Anopheles. This model contains roughly 20% of the larval images in the GLOBE database, which were deemed usable for the training model. Keywords: Anopheles, Citizen Science, convolutional neural network, image classification, mosquito habitat.
As climate change progresses, hydrological regimes of temporary and perennial water bodies are projected to change, affecting biodiversity and ecosystem functions. Researchers have successfully employed the use of satellite imagery, camera traps and site visits to map these changes in hydrological regimes. Though effective, their use can come with considerable cost at high temporal and spatial resolution. A more affordable measure in mapping hydrological regimes has been the use of data loggers of conductivity, but the use of data loggers of temperature and light intensity is uncommon. Using validated data of 213 days of the aquatic and terrestrial phases of a temporary pond, we show that temperature and light intensity data can be used to discern hydrological state. The aquatic phase had lower measures of both parameters when compared to the terrestrial phase. This was caused by the stability of the aquatic environment. The most powerful measures in discerning hydrological state were diel maximum temperature, diel temperature range, and rate of change of temperature. Greater distinctive power was obtained through the use of multiple measures of the parameters. In addition, key events such as flooding and drying were discernible within the temperature and light intensity data. High-resolution temperature and light intensity data are able to aid in understanding these dynamics of hydrological state and can be used to monitor ecosystem functions amid changes in temporary and perennial water bodies.
Self-propelled motion is an agnostic biosignature that is observed widely, yet motility of microbes in their natural environments is sparsely studied. In this study we use a Digital Holographic Microscope (DHM) for in situ imaging of aquatic samples in extreme environments to investigate motility and morphology as biosignatures. Samples were collected from glaciovolcanic ice caves, glacial runoff, hot springs, and mixed glacial and hot spring samples.The transport and deposition of materials and heat from the volcanic subsurface in glaciovolcanic caves may be similar in some respects to the eruption processes of the plumes of Enceladus. Through different tracking methods, we identified concentrations of organisms, morphologies, swimming patterns, speeds, and turn angles. In every type of sample we looked we were able to identify motile organisms. Methods for distinguishing active swimming from Browian motion and drift are considered. Field work was done over two deployments in collaboration with the Thermal High-voltage Ocean-penetrating Research platform (THOR) science team and EELS robotics team. This work and these collaborations intend to inform future off-world extant life detection missions of the utility of DHM and motility as an investigation tool and biosignature, respectively.
Genomic tools are increasingly being deployed to unlock factors affecting genetic gain. Here, we report the utility of a mid-density marker panel for genetic studies and other applications in cowpea breeding. The 2,602-marker panel was used to genotype 376 cowpea materials pooled from four different genetic backgrounds. The panel was informative with over 78% SNPs exceeding minor allele frequency of 0.20. The panel correctly deciphered co-ancestry among lines, identifying 0.04 % of all pairwise relationships as identical lines, 0.01% as parent-offspring, 0.12% as half-sibs, 39.19% as unrelated, and 60.64% with distant relationships. STRUCTURE, principal component analysis (PCA), and discriminant analysis of principal components (DAPC) inferred two major groups, with all the bi-parental RILs placed in a single gene pool. Excluding bi-parental RILs exposed sub-structure within the remaining diverse lines. Variance within populations was higher (16.64%) than that between populations (8.38%). Linkage disequilibrium (LD) decay was correctly depicted as being slower in bi-parental RILs than in other populations. Overall, LD dissipated to r2 = 0.1 at 1.25Mb. In addition, we mapped a region on chromosome VU07 known to be associated with both seed and flower colors in cowpea. This region harbors several genes including Vigun07g110700, a basic helix-loop-helix (bHLH) DNA-binding superfamily protein that regulates plant pigmentation. The panel revealed unexpected heterozygosity within some lines and authenticated the hybridity of F1 progenies. This study demonstrated the panel’s effectiveness for molecular applications in cowpea, and that the accessions that were used are genetically diverse and suitable for trait discovery and breeding.