The Pacific Arctic region is characterized by seasonal sea-ice, the spatial extent and duration of which varies considerably. In this region, diatoms are the dominant phytoplankton group during spring and summer. To facilitate survival during periods that are less favorable for growth, many diatom species produce resting stages that settle to the seafloor and can serve as a potential inoculum for subsequent blooms. Since diatom assemblage composition is closely related to sea-ice dynamics, detailed studies of biophysical interactions are fundamental to understanding the lower trophic levels of ecosystems in the Pacific Arctic. One way to explore this relationship is by comparing the distribution and abundance of diatom resting stages with patterns of sea-ice coverage. In this study, we quantified viable diatom resting stages in sediments collected during summer and autumn 2018 and explored their relationship to sea-ice extent during the previous winter and spring. Diatom assemblages were clearly dependent on the variable timing of the sea-ice retreat and accompanying light conditions. In areas where sea-ice retreated earlier, open-water species such as Chaetoceros spp. and Thalassiosira spp. were abundant. In contrast, proportional abundances of Attheya spp. and pennate diatom species that are commonly observed in sea-ice were higher in areas where diatoms experienced higher light levels and longer day length in/under the sea-ice. This study demonstrates that sea-ice dynamics are an important determinant of diatom species composition and distribution in the Pacific Arctic region.
Introduction: It has been proposed that the recently discovered superphylum of Asgard archaea may represent a historical link/bridge between the Archaea and Eukarya. The arrangement of genes in genomes is a window to understand how organisms are related. In particular, the translation machinery and the genes that encode the same have a long evolutionary history. In order to gain further insight into the evolutionary position of the Asgard archaea, the genome order of ribosomal protein coding genes was analyzed. The Asgard archaea were compared with non-Asgard archaeal and bacterial genomes. Results: A core of co-occurring 15 genes belonging to the segment of the S10 and spc cluster (which are characterized and established as operons in Bacteria), was identified as conserved in gene order, arrangement and genome location in Asgard archaea, non-Asgard archaea and Bacteria. This core occurs as a complete set in the genomes of Lokiarchaeota MK-D1 (Candidatus Prometheoarchaeum syntrophicum), and Candidatus Odinarchaeota archaeon LCB_4. The genome assemblies of the other Asgard genomes are incomplete and occur in multiple contigs (>50) and hence this cluster is found in sections across contigs, with a section often either ending or beginning a contig. The cluster organization is indicative of co-occurrence, if the genome was complete. A second smaller cluster comprising the homologs of the most conserved genes of the bacterial S10 operon/cluster namely, uS10-uL3-uL4-uL23-uL2 occurs independently on the Asgard genomes, separate from the rest, a feature shared by many non-Asgard archaea as well. Other clusters. A new cluster L7ae-infB was identified to co-occur with the minor S24e-S27ae cluster in the two complete Asgard genomes. The L7ae-infB cluster co-occurs with the S24e-S27ae cluster in some (non-Asgard) Crenarchaeota (Desulfurococcacea) and Euryarchaeota (Methanobacteriaceae), while it co-occurs with the (Alpha operon) L18e cluster in some (non-Asgard) Euryarchaeota (Halobacteriaceae). Overall, the organization of the most universal and highly conserved S10 and spc cluster in Asgard archaea resembles that of the non-Asgard Thaumarchaeota and the DPANN group. References: Wang J et al (2009) Archaea, 2(4), 241–251. Da Cunha V et al (2017) Plos Genetics 13(6), e1006810. Bowman J. C. et al. (2020) Chem Rev, 120(11), 4848-4878.
Fossil fuel combustion, land use change and other human activities have increased the atmospheric carbon dioxide (CO2) abundance by about 50% since the beginning of the industrial age. The atmospheric CO2 growth rates would have been much larger if natural sinks in the land biosphere and ocean had not removed over half of this anthropogenic CO2. As these CO2 emissions grew, uptake by the ocean increased in response to increases in atmospheric CO2 partial pressure (pCO2). On land, gross primary production (GPP) also increased, but the dynamics of other key aspects of the land carbon cycle varied regionally. Over the past three decades, CO2 uptake by intact tropical humid forests declined, but these changes are offset by increased uptake across mid- and high-latitudes. While there have been substantial improvements in our ability to study the carbon cycle, measurement and modeling gaps still limit our understanding of the processes driving its evolution. Continued ship-based observations combined with expanded deployments of autonomous platforms are needed to quantify ocean-atmosphere fluxes and interior ocean carbon storage on policy-relevant spatial and temporal scales. There is also an urgent need for more comprehensive measurements of stocks, fluxes and atmospheric CO2 in humid tropical forests and across the Arctic and boreal regions, which are experiencing rapid change. Here, we review our understanding of the atmosphere, ocean, and land carbon cycles and their interactions, identify emerging measurement and modeling capabilities and gaps and the need for a sustainable, operational framework to ensure a scientific basis for carbon management.
In 2018–2019, Central Europe experienced an unprecedented multi-year drought with severe impacts on society and ecosystems. In this study, we analyzed the impact of this drought on water quality by comparing long-term (1997-2017) nitrate export with 2018–2019 export in a heterogeneous mesoscale catchment. We combined data-driven analysis with process-based modelling to analyze nitrogen retention and the underlying mechanisms in the soils and during subsurface transport. We found a drought-induced shift in concentration-discharge relationships, reflecting exceptionally low riverine nitrate concentrations during dry periods and exceptionally high concentrations during subsequent wet periods. Nitrate loads were up to 70% higher compared to the long-term load-discharge relationship. Model simulations confirmed that this increase was driven by decreased denitrification and plant uptake and subsequent flushing of accumulated nitrogen during rewetting. Fast transit times (<2 months) during wet periods in the upstream sub-catchments enabled a fast water quality response to drought. In contrast, longer transit times downstream (>20 years) inhibited a fast response but potentially contribute to a long-term drought legacy. Overall, our study reveals that severe multi-year droughts, which are predicted to become more frequent across Europe, can reduce the nitrogen retention capacity of catchments, thereby intensifying nitrate pollution and threatening water quality.
The facultative, chemolithotrophic bacteria Hydrogenophilus thermoluteolus is an understudied thermophilic, hydrogen- and thiosulfate- oxidizing microorganism that has been found globally in hot spring environments. It was identified in a series of four soil samples collected around the Polloquere hot spring of Lauca National Park, Chile, in 10m intervals from the hot spring water line. Metagenome-assembled genomes (MAGs) of H. thermoluteolus were reconstructed from each sample, exhibiting high completion and a 98% average nucleotide identity with the reference genome of the cultured H. thermoluteolus isolate. In this study, we collected and analyzed publicly available genomes of H. thermoluteolus and other members of the Hydrogenophilceae family derived from cultures and metagenomes from a diverse set of geothermal environments for pangenomic comparison with the Polloquere MAGs. The Polloquere soils are characterized by distinct changes to the environmental chemistry and biology across the 30m distance from the hot spring. In particular, increased aridity and pH, as well as lower temperatures and biomass, coincided with a shift from a characteristic geothermal microbial population, to that of an arid desert community. Notably, however, the presence and relative abundance of H. thermoluteolus remained stable over the same distance (~0.1% of the total community). Using pangenomics, we were able to deduce several genomic differences between soil samples closest (0m) and furthest (30m) from the hot spring, as well as between the Polloquere MAGs and the cultured reference. Functionally, the 30m MAG lacked carbon fixation capabilities, while all of the soil MAGs showed added genomic capacity for denitrification not present in the reference genome. These results contribute significantly to the pool of genomic data for H. thermoluteolus, adding to our understanding of the organism’s high metabolic flexibility. The Polloquere MAGs also represent a rare example of this organism appearing in a dry, colder, soil environment, presumably transported from the local hot spring. This study investigates how the genomes and metabolisms of H. thermoluteolus vary between environments from a biogeographical perspective, both globally and across a small spatial distance defined by a steep environmental gradient.
Low streamflows can increase vulnerability to warming, impacting coldwater fish. Water managers need tools to quantify these impacts and predict future water temperatures. Contrary to most statistical models’ assumptions, many seasonally changing factors (e.g., water sources and solar radiation) cause relationships between flow and water temperature to vary throughout the year. Using 21 years of air temperature and flow data, we modeled daily water temperatures in California’s snowmelt-driven Scott River where agricultural diversions consume most summer surface flows. We used generalized additive models to test time-varying and nonlinear effects of flow on water temperatures. Models that represented seasonally varying flow effects with intermediate complexity outperformed simpler models assuming constant relationships between water temperature and flow. Cross-validation error of the selected model was ≤1.2 °C. Flow variation had stronger effects on water temperatures in April–July than in other months. We applied the model to predict effects of instream flow scenarios proposed by regulatory agencies. Relative to historic conditions, the higher instream flow scenario would reduce annual maximum temperature from 25.2 °C to 24.1 °C, reduce annual exceedances of 22 °C (a cumulative thermal stress metric) from 106 to 51 degree-days, and delay onset of water temperatures >22 °C during some drought years. Testing the same modeling approach at nine additional sites showed similar accuracy and flow effects. These methods can be applied to streams with long-term flow and water temperature records to fill data gaps, identify periods of flow influence, and predict temperatures under flow management scenarios.
Clumped isotope thermometry can independently constrain the formation temperatures of carbonates, but a lack of precisely temperature-controlled calibration samples limits its application on aragonites. To address this issue, we present clumped isotope compositions of aragonitic bivalve shells grown under highly controlled temperatures (1‒18°C), which we combine with clumped isotope data from natural and synthetic aragonites from a wide range of temperatures (1‒850°C). We observe no discernible offset in clumped isotope values between aragonitic foraminifera, mollusks, and abiogenic aragonites or between aragonites and calcites, eliminating the need for a mineral-specific calibration or acid fractionation factor. However, due to non-linear behavior of the clumped isotope thermometer, including high-temperature (>100°C) datapoints in linear clumped isotope calibrations causes them to underestimate temperatures of cold (1‒18°C) carbonates by 2.7 ± 2.0°C (95% confidence level). Therefore, clumped isotope-based paleoclimate reconstructions should be calibrated using samples with well constrained formation temperatures close to those of the samples.
Earthworms play a critical role in soil ecosystems. Analyzing the spatial structure of earthworm burrows is important to understand their impact on water flow and solute transport. Existing in-situ extraction methods for earthworm burrows are time-consuming, labor-intensive and inaccurate, while CT scanning imaging is complex and expensive. The aim of this study was to quantitatively characterize structural characteristics (cross-sectional area (A), circularity (C), diameter (D), actual length (Lt), tortuosity (τ)) of anecic earthworm burrows that were open and connected at the soil surface at two sites of different tillage treatments (no-till at Lu Yuan (LY) and rotary tillage at Shang Zhuang (SZ)) by combining a new in-situ tin casting method with three-dimensional (3D) laser scanning technology. The cross-sections of anecic earthworm burrows were almost circular, and the C values were significantly negatively correlated with D and A. Statistically, there were no significant differences in the τ values (1.143 ± 0.082 vs 1.133 ± 0.108) of anecic earthworm burrows at LY and SZ, but D (6.456 ± 1.585 mm) and A (36.929 ± 21.656 mm2) of anecic earthworm burrows at LY were significantly larger than D (3.449 ± 0.531 mm) and A (9.786 ± 2.885 mm2) at SZ. Our study showed that burrow structures at two different sites differed from each other. Soil tillage methods, soil texture and soil organic matter content at the two sites could have impacted earthworm species composition, variation of earthworm size and the morphology of burrows. The method used in this research enabled us to adequately assess the spatial structure of anecic earthworm burrows in the field with a limited budget.
The COVID-19 pandemic has put unprecedented pressure on public health resources around the world. From adversity opportunities have arisen to measure the state and dynamics of human disease at a scale not seen before. Early in the COVID-19 epidemic scientists and engineers demonstrated the use of wastewater as a medium by which the virus could be monitored both temporally and spatially. In the United Kingdom this evidence prompted the development of National wastewater surveillance programmes involving UK Government agencies academics and private companies. In terms of speed and scale the programmes have proven to be unique in its efforts to deliver measures of virus dynamics across a large proportion of the populations in all four regions of the country. This success has demonstrated that wastewater-based epidemiology (WBE) can be a critical component in public health protection at regional and national levels and looking beyond COVID-19 is likely to be a core tool in monitoring and informing on a range of biological and chemical markers of human health; some established (e.g. pharmaceutical usage) and some emerging (e.g. metabolites of stress). We present here a discussion of uncertainty and variation associated with surveillance of wastewater focusing on lessons-learned from the UK programmes monitoring COVID-19 but addressing the areas that can broadly be applied to WBE more generally. Through discussion and the use of case studies we highlight that sources of uncertainty and variability that can impact measurement quality and importantly interpretation of data for public health decision-making are varied and complex. While some factors remain poorly understood and require dedicated research we present approaches taken by the UK programmes to manage and mitigate the more tractable components. This work provides a platform to integrate uncertainty management through data analysis quality assurance and modelling into the inevitable expansion of WBE activities as part of One Health initiatives.
Roots are the interface between the plant and the soil and play a central role in multiple ecosystem processes. With intensification of agricultural practices, rhizosphere processes are being disrupted and are causing degradation of the physical, chemical, and biotic properties of soil. Improvement of ecosystem service performance is rarely considered as a breeding trait due to the complexities and challenges of belowground evaluation. Advancements in root phenotyping and genetic tools are critical in accelerating ecosystem service improvement in cover crops. Here I will present root phenotyping approaches for assessing ecosystem service in a prospective cash cover crop; pennycress (Thlaspi arvense L.). In development is a large format mesocosm system that will allow 3D root system architecture analysis of multiple plants. Using this system, we will be assessing how variation in pennycress root system architecture can affect ecosystem service and abiotic stress tolerance with the plant to scale from single plant to canopy level traits.
Mung bean [Vigna radiata (L.) Wilczek] is a drought-tolerant, short-duration crop, and a rich source of protein and other valuable minerals, vitamins, and antioxidants. The main objectives of this research were (1) to study the root traits related with the phenotypic and genetic diversity of 375 mung bean genotypes of the Iowa (IA) diversity panel and (2) to conduct genome-wide association studies of root-related traits using the Automated Root Image Analysis (ARIA) software. We collected over 9,000 digital images at three-time points (days 12, 15, and 18 after germination). A broad sense heritability for days 15 (0.22–0.73) and 18 (0.23–0.87) was higher than that for day 12 (0.24–0.51). We also reported root ideotype classification, i.e., PI425425 (India), PI425045 (Philippines), PI425551 (Korea), PI264686 (Philippines), and PI425085 (Sri Lanka) that emerged as the top five in the topsoil foraging category, while PI425594 (unknown origin), PI425599 (Thailand), PI425610 (Afghanistan), PI425485 (India), and AVMU0201 (Taiwan) were top five in the drought-tolerant and nutrient uptake “steep, cheap, and deep” ideotype. We identified promising genotypes that can help diversify the gene pool of mung bean breeding stocks and will be useful for further field testing. Using association studies, we identified markers showing significant associations with the lateral root angle (LRA) on chromosomes 2, 6, 7, and 11, length distribution (LED) on chromosome 8, and total root length-growth rate (TRL_GR), volume (VOL), and total dry weight (TDW) on chromosomes 3 and 5. We discussed genes that are potential candidates from these regions. We reported beta-galactosidase 3 associated with the LRA, which has previously been implicated in the adventitious root development via transcriptomic studies in mung bean. Results from this work on the phenotypic characterization, root-based ideotype categories, and significant molecular markers associated with important traits will be useful for the marker-assisted selection and mung bean improvement through breeding.
Quantifying phenotypes of root-root interactions would allow a greater understanding of how plants react to belowground competition through plasticity of architectural traits. Past research has shown that plants will over proliferate roots in the presence of competition, leaving less resources to allocate above ground, negatively impacting shoot growth and yields . Further evidence highlights a response to neighboring plants in the root architecture of Arabidposis thaliana, as individuals concentrate root mass towards their competitors . To visualize and quantify root architecture plasticity involved in these root-root interactions in real soil, we developed a modified mesocosm system. Within the mesocosm box common bean (Phaseolus vulgaris) seeds were germinated 10 inches apart from each other. Mesh screens were placed on either side of each bean, in order to capture root growth towards each other and/or away from each other. Plants were harvested at the 7-week mark, when the root archetype of each individual was developed and prominent. To harvest our boxes three sides of the mesocosm box was removed and the soil was washed away. Images of each box was taken to produce 3D models. Improvements to the mesocosm system will be made to reliably extract root traits in real soil. These experiments will shine light on an understudied section of crop science and will allow farmers and researchers a better understanding of an otherwise unseen phenomena.
Arsenic (As) pollutes large regions of Asia. Despite phytoremediation initiatives using hyperaccumulators to remove As from contaminated soil, farmers remain reluctant to employ such strategies because of the low biomass and economic value of hyperaccumulating plants. In this study, we demonstrate that cassava can be used for As remediation using a high-throughput root phenotyping platform for cassava roots that we previously developed . Using this phenotyping platform, we identified contrasting root traits associated with As uptake for the two genotypes Rayong 11(R11) and Rayong 90 (R90). Both cassava varieties were grown in pot systems under control (0 mg kg-1 As) and high As (50 mg kg-1 As) conditions and harvested 120 days after planting. We found As stress to reduce shoot and plant dry weight by 57% and 53%, respectively, whereas root dry weight and root traits showed only a slight change. Under As stress, R11 had a 75% higher nodal root number and a 59% lower basal root number than R90. Moreover, R11 root (100 mg kg-1 As) and branch (9 mg kg-1 As) tissues had considerably higher As concentrations than the same tissues in R90. The bioaccumulation coefficient for R11 (2.1) was significantly greater than for R90 (0.9). Additionally, bioethanol yields were unaffected by the presence of As in cassava starch. We suggest that cassava is a promising crop for phytoremediation and that root phenotyping is essential to breed cassava varieties with enhanced As uptake.
Understanding root traits is essential to improve water uptake, increase nitrogen capture and raise carbon sequestration from the atmosphere. However, high-throughput phenotyping to quantify root traits for deeper field-grown roots remain a challenge. Recently developed open-source methods use image-based 3D reconstruction algorithms to build 3D models of plant roots from multiple 2D images and can extract root traits and phenotypes. Most of these methods rely on automated image orientation (Structure from Motion) and dense image matching (Multiple View Stereo) algorithms to produce a 3D point cloud or mesh model from 2D images. Until now it is not known how the performance of these methods compares to each other when applied to field-grown roots. We investigate commonly used open-source pipelines on a test panel of twelve contrasting maize genotypes grown in real field conditions in this comparison study [2-6]. We compare 3D point clouds in terms of number of points, computation time, and model surface density. This comparison study will provide insight into the performance of different open-source pipelines for maize root phenotyping, and illuminates trade-offs between 3D model quality and performance cost for future high-throughput 3D root phenotyping.
The development of new phenomics approaches to image and process data from the subcellular to ecosystem-scale has accelerated over the past decade. Many of these tools are produced “in-house” within a single lab or a group of collaborating labs, making it hard to keep up with the state-of-the-art for phenomics hardware and software development. The Plant Cell Atlas Phenomics Committee is creating a collaborative space that connects phenomics developers with each other and with the greater plant science community, with the goal of facilitating wide-reaching collaborations. To do this, we will be hosting a video series called “How We Built It” where developers provide a short tour of their inventions and relevant biological applications. We will follow the series with a more in-depth networking event where plant scientists can connect with the inventors and discuss collaborative opportunities. Our goal is to streamline the invention of new phenotyping tools and broaden the application of existing tools.
Committees touch nearly every facet in the science, technology, engineering, and mathematics (STEM) research enterprise. However, the role of gatekeeping through committee work has received little attention in Earth and space sciences. We propose a novel concept called, “regenerative gatekeeping” to challenge institutional inertia, cultivate belonging, accessibility, justice, diversity, equity, and inclusion in committee work. Three examples, a hiring committee process, a seminar series innovation, and an awards committee, highlight the need to self-assess policies and practices, ask critical questions and engage in generative conflict. Rethinking committee work can activate distributed mechanisms needed to promote change.
India has witnessed a five-fold increase in dengue incidence in the past decade. However, the nation-wide distribution of dengue vectors, and the impacts of climate change are not known. In this study, species distribution modelling was used to predict the baseline and future distribution of Aedine vectors in India on the basis of biologically relevant climatic indicators. Known occurrences of Aedes aegypti and Aedes albopictus were obtained from the Global Biodiversity Information Facility database and previous literature. Bio-climatic variables were used as the potential predictors of vector distribution. After eliminating collinear and low contributing predictors, the baseline and future prevalence of Aedes aegypti and Aedes albopictus was determined, under three Representative Concentration Pathway scenarios (RCP 2.6, RCP 4.5 and RCP 8.5), using the MaxEnt species distribution model. Aedes aegypti was found prevalent in most parts of the southern peninsula, the eastern coastline, north eastern states and the northern plains. In contrast, Aedes albopictus has localized distribution along the eastern and western coastlines, north eastern states and in the lower Himalayas. Under future scenarios of climate change, Aedes aegypti is projected to expand into unsuitable regions of the Thar desert, whereas Aedes albopictus is projected to expand to the upper and trans Himalaya regions of the north. Overall, the results provide a reliable assessment of vectors prevalence in most parts of the country that can be used to guide surveillance efforts, despite minor disagreements with dengue incidence in Rajasthan and the north east, possibly due to behavioural practices and sampling efforts.
Earth’s Critical Zone (CZ), the near-surface layer where rock is weathered and landscapes co-evolve with life, is profoundly influenced by the type of underlying bedrock. Previous studies employing the CZ framework have focused almost exclusively on landscapes dominated by silicate rocks. However, carbonate rocks crop out on approximately 15% of Earth’s ice-free continental surface and provide important water resources and ecosystem services to ~1.2 billion people. Unlike silicates, carbonate minerals weather congruently and have high solubilities and rapid dissolution kinetics, enabling the development of large, interconnected pore spaces and preferential flow paths that restructure the CZ. Here we review the state of knowledge of the carbonate CZ, exploring parameters that produce contrasts in the CZ in different carbonate settings and identifying important open questions about carbonate CZ processes. We introduce the concept of a carbonate-silicate CZ spectrum and examine whether current conceptual models of the CZ, such as the conveyor model, can be applied to carbonate landscapes.We argue that, to advance beyond site-specific understanding and develop a more general conceptual framework for the role of carbonates in the CZ, we need integrative studies spanning both the carbonate-silicate spectrum and a range of carbonate settings.