Automating methods for estimating metabolite volatility.

The volatility of metabolites can influence their biological roles and inform optimal methods for their detection. Yet, volatility information is not readily available for the large number of described metabolites, limiting the exploration of volatility as a fundamental trait of metabolites. Here, we adapted methods to estimate vapor pressure from the functional group composition of individual molecules (SIMPOL.1) to predict the gas-phase partitioning of compounds in different environments. We implemented these methods in a new open pipeline called volcalc that uses chemoinformatic tools to automate these volatility estimates for all metabolites in an extensive and continuously updated pathway database: the Kyoto Encyclopedia of Genes and Genomes (KEGG) that connects metabolites, organisms, and reactions. We first benchmark the automated pipeline against a manually curated data set and show that the same category of volatility (e.g., nonvolatile, low, moderate, high) is predicted for 93% of compounds. We then demonstrate how volcalc might be used to generate and test hypotheses about the role of volatility in biological systems and organisms. Specifically, we estimate that 3.4 and 26.6% of compounds in KEGG have high volatility depending on the environment (soil vs. clean atmosphere, respectively) and that a core set of volatiles is shared among all domains of life (30%) with the largest proportion of kingdom-specific volatiles identified in bacteria. With volcalc, we lay a foundation for uncovering the role of the volatilome using an approach that is easily integrated with other bioinformatic pipelines and can be continually refined to consider additional dimensions to volatility. The volcalc package is an accessible tool to help design and test hypotheses on volatile metabolites and their unique roles in biological systems.

Global Wheat Head Detection 2021: An Improved Dataset for Benchmarking Wheat Head Detection Methods.

The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities. From this first experience, a few avenues for improvements have been identified regarding data size, head diversity, and label reliability. To address these issues, the 2020 dataset has been reexamined, relabeled, and complemented by adding 1722 images from 5 additional countries, allowing for 81,553 additional wheat heads. We now release in 2021 a new version of the Global Wheat Head Detection dataset, which is bigger, more diverse, and less noisy than the GWHD_2020 version.

Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data-model integration.

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data-model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model-data benchmarking; and data assimilation and ecological forecasting. This community-driven approach is a key to meeting the pressing needs of science and society in the 21st century.

Robust paths to net greenhouse gas mitigation and negative emissions via advanced biofuels.

Biofuel and bioenergy systems are integral to most climate stabilization scenarios for displacement of transport sector fossil fuel use and for producing negative emissions via carbon capture and storage (CCS). However, the net greenhouse gas mitigation benefit of such pathways is controversial due to concerns around ecosystem carbon losses from land use change and foregone sequestration benefits from alternative land uses. Here, we couple bottom-up ecosystem simulation with models of cellulosic biofuel production and CCS in order to track ecosystem and supply chain carbon flows for current and future biofuel systems, with comparison to competing land-based biological mitigation schemes. Analyzing three contrasting US case study sites, we show that on land transitioning out of crops or pasture, switchgrass cultivation for cellulosic ethanol production has per-hectare mitigation potential comparable to reforestation and severalfold greater than grassland restoration. In contrast, harvesting and converting existing secondary forest at those sites incurs large initial carbon debt requiring long payback periods. We also highlight how plausible future improvements in energy crop yields and biorefining technology together with CCS would achieve mitigation potential 4 and 15 times greater than forest and grassland restoration, respectively. Finally, we show that recent estimates of induced land use change are small relative to the opportunities for improving system performance that we quantify here. While climate and other ecosystem service benefits cannot be taken for granted from cellulosic biofuel deployment, our scenarios illustrate how conventional and carbon-negative biofuel systems could make a near-term, robust, and distinctive contribution to the climate challenge.

BrAPI-an application programming interface for plant breeding applications.

MOTIVATION: Modern genomic breeding methods rely heavily on very large amounts of phenotyping and genotyping data, presenting new challenges in effective data management and integration. Recently, the size and complexity of datasets have increased significantly, with the result that data are often stored on multiple systems. As analyses of interest increasingly require aggregation of datasets from diverse sources, data exchange between disparate systems becomes a challenge. RESULTS: To facilitate interoperability among breeding applications, we present the public plant Breeding Application Programming Interface (BrAPI). BrAPI is a standardized web service API specification. The development of BrAPI is a collaborative, community-based initiative involving a growing global community of over a hundred participants representing several dozen institutions and companies. Development of such a standard is recognized as critical to a number of important large breeding system initiatives as a foundational technology. The focus of the first version of the API is on providing services for connecting systems and retrieving basic breeding data including germplasm, study, observation, and marker data. A number of BrAPI-enabled applications, termed BrAPPs, have been written, that take advantage of the emerging support of BrAPI by many databases. AVAILABILITY AND IMPLEMENTATION: More information on BrAPI, including links to the specification, test suites, BrAPPs, and sample implementations is available at https://brapi.org/. The BrAPI specification and the developer tools are provided as free and open source.

A New Optical Remote Sensing Technique for High-Resolution Mapping of Soil Moisture.

The recently developed OPtical TRApezoid Model (OPTRAM) has been successfully applied for watershed scale soil moisture (SM) estimation based on remotely sensed shortwave infrared (SWIR) transformed reflectance (TRSWIR) and the normalized difference vegetation index (NDVI). This study is aimed at the evaluation of OPTRAM for field scale precision agriculture applications using ultrahigh spatial resolution optical observations obtained with one of the world's largest field robotic phenotyping scanners located in Maricopa, Arizona. We replaced NDVI with the soil adjusted vegetation index (SAVI), which has been shown to be more accurate for cropped agricultural fields that transition from bare soil to dense vegetation cover. The OPTRAM was parameterized based on the trapezoidal geometry of the pixel distribution within the TRSWIR-SAVI space, from which wet- and dry-edge parameters were determined. The accuracy of the resultant SM estimates is evaluated based on a comparison with ground reference measurements obtained with Time Domain Reflectometry (TDR) sensors deployed to monitor surface, near-surface and root zone SM. The obtained results indicate an SM estimation error between 0.045 and 0.057 cm3 cm-3 for the near-surface and root zone, respectively. The high resolution SM maps clearly capture the spatial SM variability at the sensor locations. These findings and the presented framework can be applied in conjunction with Unmanned Aerial System (UAS) observations to assist with farm scale precision irrigation management to improve water use efficiency of cropping systems and conserve water in water-limited regions of the world.

ForC: a global database of forest carbon stocks and fluxes.

Forests play an influential role in the global carbon (C) cycle, storing roughly half of terrestrial C and annually exchanging with the atmosphere more than five times the carbon dioxide (CO2 ) emitted by anthropogenic activities. Yet, scaling up from field-based measurements of forest C stocks and fluxes to understand global scale C cycling and its climate sensitivity remains an important challenge. Tens of thousands of forest C measurements have been made, but these data have yet to be integrated into a single database that makes them accessible for integrated analyses. Here we present an open-access global Forest Carbon database (ForC) containing previously published records of field-based measurements of ecosystem-level C stocks and annual fluxes, along with disturbance history and methodological information. ForC expands upon the previously published tropical portion of this database, TropForC (https://doi.org/10.5061/dryad.t516f), now including 17,367 records (previously 3,568) representing 2,731 plots (previously 845) in 826 geographically distinct areas. The database covers all forested biogeographic and climate zones, represents forest stands of all ages, and currently includes data collected between 1934 and 2015. We expect that ForC will prove useful for macroecological analyses of forest C cycling, for evaluation of model predictions or remote sensing products, for quantifying the contribution of forests to the global C cycle, and for supporting international efforts to inventory forest carbon and greenhouse gas exchange. A dynamic version of ForC is maintained at on GitHub (https://GitHub.com/forc-db), and we encourage the research community to collaborate in updating, correcting, expanding, and utilizing this database. ForC is an open access database, and we encourage use of the data for scientific research and education purposes. Data may not be used for commercial purposes without written permission of the database PI. Any publications using ForC data should cite this publication and Anderson-Teixeira et al. (2016a) (see Metadata S1). No other copyright or cost restrictions are associated with the use of this data set.

Carbon dynamics of mature and regrowth tropical forests derived from a pantropical database (TropForC-db).

Tropical forests play a critical role in the global carbon (C) cycle, storing ~45% of terrestrial C and constituting the largest component of the terrestrial C sink. Despite their central importance to the global C cycle, their ecosystem-level C cycles are not as well-characterized as those of extra-tropical forests, and knowledge gaps hamper efforts to quantify C budgets across the tropics and to model tropical forest-climate interactions. To advance understanding of C dynamics of pantropical forests, we compiled a new database, the Tropical Forest C database (TropForC-db), which contains data on ground-based measurements of ecosystem-level C stocks and annual fluxes along with disturbance history. This database currently contains 3568 records from 845 plots in 178 geographically distinct areas, making it the largest and most comprehensive database of its type. Using TropForC-db, we characterized C stocks and fluxes for young, intermediate-aged, and mature forests. Relative to existing C budgets of extra-tropical forests, mature tropical broadleaf evergreen forests had substantially higher gross primary productivity (GPP) and ecosystem respiration (Reco), their autotropic respiration (Ra) consumed a larger proportion (~67%) of GPP, and their woody stem growth (ANPPstem) represented a smaller proportion of net primary productivity (NPP, ~32%) or GPP (~9%). In regrowth stands, aboveground biomass increased rapidly during the first 20 years following stand-clearing disturbance, with slower accumulation following agriculture and in deciduous forests, and continued to accumulate at a slower pace in forests aged 20-100 years. Most other C stocks likewise increased with stand age, while potential to describe age trends in C fluxes was generally data-limited. We expect that TropForC-db will prove useful for model evaluation and for quantifying the contribution of forests to the global C cycle. The database version associated with this publication is archived in Dryad (DOI: 10.5061/dryad.t516f) and a dynamic version is maintained at https://github.com/forc-db.

Plants in silico: why, why now and what?--an integrative platform for plant systems biology research.

A paradigm shift is needed and timely in moving plant modelling from largely isolated efforts to a connected community endeavour that can take full advantage of advances in computer science and in mechanistic understanding of plant processes. Plants in silico (Psi) envisions a digital representation of layered dynamic modules, linking from gene networks and metabolic pathways through to cellular organization, tissue, organ and whole plant development, together with resource capture and use efficiency in dynamic competitive environments, ultimately allowing a mechanistically rich simulation of the plant or of a community of plants in silico. The concept is to integrate models or modules from different layers of organization spanning from genome to phenome to ecosystem in a modular framework allowing the use of modules of varying mechanistic detail representing the same biological process. Developments in high-performance computing, functional knowledge of plants, the internet and open-source version controlled software make achieving the concept realistic. Open source will enhance collaboration and move towards testing and consensus on quantitative theoretical frameworks. Importantly, Psi provides a quantitative knowledge framework where the implications of a discovery at one level, for example, single gene function or developmental response, can be examined at the whole plant or even crop and natural ecosystem levels.

Toward systems-level analysis of agricultural production from crassulacean acid metabolism (CAM): scaling from cell to commercial production.

Systems-level analyses have become prominent tools for assessing the yield, viability, economic consequences and environmental impacts of agricultural production. Such analyses are well-developed for many commodity crops that are used for food and biofuel, but have not been developed for agricultural production systems based on drought-tolerant plants that use crassulacean acid metabolism (CAM). We review the components of systems-level evaluations, and identify the information available for completing such analyses for CAM cropping systems. Specific needs for developing systems-level evaluations of CAM agricultural production include: improvement of physiological models; assessment of product processing after leaving the farm gate; and application of newly available genetic tools to the optimization of CAM species for commercial production.

A physiological and biophysical model of coppice willow (Salix spp.) production yields for the contiguous USA in current and future climate scenarios.

High-performance computing has facilitated development of biomass production models that capture the key mechanisms underlying production at high spatial and temporal resolution. Direct responses to increasing [CO2 ] and temperature are important to long-lived emerging woody bioenergy crops. Fast-growing willow (Salix spp.) within short rotation coppice (SRC) has considerable potential as a renewable biomass source, but performance over wider environmental conditions and under climate change is uncertain. We extended the bioenergy crop modeling platform, BioCro, to SRC willow by adding coppicing and C3 photosynthesis subroutines, and modifying subroutines for perennation, allocation, morphology, phenology and development. Parameterization with measurements of leaf photosynthesis, allocation and phenology gave agreement of modeled with measured yield across 23 sites in Europe and North America. Predictions for the continental USA suggest yields of ≥17 Mg ha(-1) year(-1) in a 4 year rotation. Rising temperature decreased predicted yields, an effect partially ameliorated by rising [CO2 ]. This model, based on over 100 equations describing the physiological and biophysical mechanisms underlying production, provides a new framework for utilizing mechanism of plant responses to the environment, including future climates. As an open-source tool, it is made available here as a community resource for further application, improvement and adaptation.

Light to liquid fuel: theoretical and realized energy conversion efficiency of plants using crassulacean acid metabolism (CAM) in arid conditions.

There has been little attention paid to crassulacean acid metabolism (CAM) as a mechanism for bioenergy crop tolerance to water limitation, in part, because potential yields of CAM plants have been assumed to be lower than those of most commonly studied bioenergy crops. The photochemical efficiency, water-use efficiency (WUE), biomass production, and fuel yield potentials of CAM, C3, and C4 plants that are considered or already in use for bioenergy are reviewed here. The theoretical photosynthetic efficiency of CAM plants can be similar to or greater than other photosynthetic pathways. In arid conditions, the greater WUE of CAM species results in theoretical biomass yield potentials that are 147% greater than C4 species. The realized yields of CAM plants are similar to the theoretical yields that account for water-limiting conditions. CAM plants can potentially be viable commercial bioenergy crops, but additional direct yield measurements from field trials of CAM species are still needed.

Predicting yields of short-rotation hybrid poplar (Populus spp.) for the United States through model-data synthesis.

Hybrid poplar (Populus spp.) is an important biomass crop being evaluated for cellulosic ethanol production. Predictions of poplar growth, rotation period, and soil carbon sequestration under various growing conditions, soils, and climates are critical for farmers and managers planning to establish short-rotation forestry (SRF) plantations. In this study, we used an ecoinformatics workflow, the Predictive Ecosystem Analyzer (PEcAn), to integrate literature data and field measurements into the Ecosystem Demography 2 (ED2) model to estimate yield potential of poplar plantations. Within PEcAn 164 records of seven different traits from the literature were assimilated using a Bayesian meta-analysis. Next, variance decomposition identified seven variables for further constraint that contributed > 80% to the uncertainty in modeled yields: growth respiration, dark respiration, quantum efficiency, mortality coefficient, water conductance, fine-root allocation, and root turnover rate. Assimilation of observed yields further constrained uncertainty in model parameters (especially dark respiration and root turnover rate) and biomass estimates. Additional measurements of growth respiration, mortality, water conductance, and quantum efficiency would provide the most efficient path toward further constraint of modeled yields. Modeled validation demonstrated that ED2 successfully captured the interannual and spatial variability of poplar yield observed at nine independent sites. Site-level analyses were conducted to estimate the effect of land use change to SRF poplar on soil C sequestration compared to alternate land uses. These suggest that poplar plantations became a C sink within 18 years of conversion from corn production or existing forest. Finally, poplar yields were estimated for the contiguous United States at a half degree resolution in order to determine potential productivity, estimate the optimal rotation period, and compare poplar to perennial grass yields. This regional projection suggests that poplar yield varies considerably with differences in soil and climate, reaching as much as 18 Mg x ha(-1) x yr(-1) in eastern, southern, and northwest regions. In New England, the upper Midwest, and northern California, yields are predicted to exceed those of the highly productive C4 perennial grass, Miscanthus. In these poplar-productive regions, 4-11 year rotations give the highest potential yields. In conclusion, poplar plantations are predicted to have a high yield potential across a wide range of climates and soils and could be sustainable in soil C sequestration.

On improving the communication between models and data.

The potential for model-data synthesis is growing in importance as we enter an era of 'big data', greater connectivity and faster computation. Realizing this potential requires that the research community broaden its perspective about how and why they interact with models. Models can be viewed as scaffolds that allow data at different scales to inform each other through our understanding of underlying processes. Perceptions of relevance, accessibility and informatics are presented as the primary barriers to broader adoption of models by the community, while an inability to fully utilize the breadth of expertise and data from the community is a primary barrier to model improvement. Overall, we promote a community-based paradigm to model-data synthesis and highlight some of the tools and techniques that facilitate this approach. Scientific workflows address critical informatics issues in transparency, repeatability and automation, while intuitive, flexible web-based interfaces make running and visualizing models more accessible. Bayesian statistics provides powerful tools for assimilating a diversity of data types and for the analysis of uncertainty. Uncertainty analyses enable new measurements to target those processes most limiting our predictive ability. Moving forward, tools for information management and data assimilation need to be improved and made more accessible.

Nitrogen limitation of net primary productivity in terrestrial ecosystems is globally distributed.

Our meta-analysis of 126 nitrogen addition experiments evaluated nitrogen (N) limitation of net primary production (NPP) in terrestrial ecosystems. We tested the hypothesis that N limitation is widespread among biomes and influenced by geography and climate. We used the response ratio (R approximately equal ANPP(N)/ANPP(ctrl)) of aboveground plant growth in fertilized to control plots and found that most ecosystems are nitrogen limited with an average 29% growth response to nitrogen (i.e., R = 1.29). The response ratio was significant within temperate forests (R = 1.19), tropical forests (R = 1.60), temperate grasslands (R = 1.53), tropical grasslands (R = 1.26), wetlands (R = 1.16), and tundra (R = 1.35), but not deserts. Eight tropical forest studies had been conducted on very young volcanic soils in Hawaii, and this subgroup was strongly N limited (R = 2.13), which resulted in a negative correlation between forest R and latitude. The degree of N limitation in the remainder of the tropical forest studies (R = 1.20) was comparable to that of temperate forests, and when the young Hawaiian subgroup was excluded, forest R did not vary with latitude. Grassland response increased with latitude, but was independent of temperature and precipitation. These results suggest that the global N and C cycles interact strongly and that geography can mediate ecosystem response to N within certain biome types.

Application of real-time PCR to study effects of ammonium on population size of ammonia-oxidizing bacteria in soil.

Ammonium oxidation by autotrophic ammonia-oxidizing bacteria (AOB) is a key process in agricultural and natural ecosystems and has a large global impact. In the past, the ecology and physiology of AOB were not well understood because these organisms are notoriously difficult to culture. Recent applications of molecular techniques have advanced our knowledge of AOB, but the necessity of using PCR-based techniques has made quantitative measurements difficult. A quantitative real-time PCR assay targeting part of the ammonia-monooxygenase gene (amoA) was developed to estimate AOB population size in soil. This assay has a detection limit of 1.3 x 10(5) cells/g of dry soil. The effect of the ammonium concentration on AOB population density was measured in soil microcosms by applying 0, 1.5, or 7.5 mM ammonium sulfate. AOB population size and ammonium and nitrate concentrations were monitored for 28 days after (NH4)2SO4 application. AOB populations in amended treatments increased from an initial density of approximately 4 x 10(6) cells/g of dry soil to peak values (day 7) of 35 x 10(6) and 66 x 10(6) cells/g of dry soil in the 1.5 and 7.5 mM treatments, respectively. The population size of total bacteria (quantified by real-time PCR with a universal bacterial probe) remained between 0.7 x 10(9) and 2.2 x 10(9) cells/g of soil, regardless of the ammonia concentration. A fertilization experiment was conducted in a tomato field plot to test whether the changes in AOB density observed in microcosms could also be detected in the field. AOB population size increased from 8.9 x 10(6) to 38.0 x 10(6) cells/g of soil by day 39. Generation times were 28 and 52 h in the 1.5 and 7.5 mM treatments, respectively, in the microcosm experiment and 373 h in the ammonium treatment in the field study. Estimated oxidation rates per cell ranged initially from 0.5 to 25.0 fmol of NH4+ h(-1) cell(-1) and decreased with time in both microcosms and the field. Growth yields were 5.6 x 10(6), 17.5 x 10(6), and 1.7 x 10(6) cells/mol of NH4+ in the 1.5 and 7.5 mM microcosm treatments and the field study, respectively. In a second field experiment, AOB population size was significantly greater in annually fertilized versus unfertilized soil, even though the last ammonium application occurred 8 months prior to measurement, suggesting a long-term effect of ammonium fertilization on AOB population size.