Biogeochemistry of upland to wetland soils, sediments, and surface waters across Mid-Atlantic and Great Lakes coastal interfaces.

Transferable and mechanistic understanding of cross-scale interactions is necessary to predict how coastal systems respond to global change. Cohesive datasets across geographically distributed sites can be used to examine how transferable a mechanistic understanding of coastal ecosystem control points is. To address the above research objectives, data were collected by the EXploration of Coastal Hydrobiogeochemistry Across a Network of Gradients and Experiments (EXCHANGE) Consortium - a regionally distributed network of researchers that collaborated on experimental design, methodology, collection, analysis, and publication. The EXCHANGE Consortium collected samples from 52 coastal terrestrial-aquatic interfaces (TAIs) during Fall of 2021. At each TAI, samples collected include soils from across a transverse elevation gradient (i.e., coastal upland forest, transitional forest, and wetland soils), surface waters, and nearshore sediments across research sites in the Great Lakes and Mid-Atlantic regions (Chesapeake and Delaware Bays) of the continental USA. The first campaign measures surface water quality parameters, bulk geochemical parameters on water, soil, and sediment samples, and physicochemical parameters of sediment and soil.

The influence of increasing atmospheric CO2 , temperature, and vapor pressure deficit on seawater-induced tree mortality.

Increasing seawater exposure is killing coastal trees globally, with expectations of accelerating mortality with rising sea levels. However, the impact of concomitant changes in atmospheric CO2 concentration, temperature, and vapor pressure deficit (VPD) on seawater-induced tree mortality is uncertain. We examined the mechanisms of seawater-induced mortality under varying climate scenarios using a photosynthetic gain and hydraulic cost optimization model validated against observations in a mature stand of Sitka spruce (Picea sitchensis) trees in the Pacific Northwest, USA, that were dying from recent seawater exposure. The simulations matched well with observations of photosynthesis, transpiration, nonstructural carbohydrates concentrations, leaf water potential, the percentage loss of xylem conductivity, and stand-level mortality rates. The simulations suggest that seawater-induced mortality could decrease by c. 16.7% with increasing atmospheric CO2 levels due to reduced risk of carbon starvation. Conversely, rising VPD could increase mortality by c. 5.6% because of increasing risk of hydraulic failure. Across all scenarios, seawater-induced mortality was driven by hydraulic failure in the first 2 yr after seawater exposure began, with carbon starvation becoming more important in subsequent years. Changing CO2 and climate appear unlikely to have a significant impact on coastal tree mortality under rising sea levels.

Deciphering the microbial and molecular responses of geographically diverse Setaria accessions grown in a nutrient-poor soil.

The microbial and molecular characterization of the ectorhizosphere is an important step towards developing a more complete understanding of how the cultivation of biofuel crops can be undertaken in nutrient poor environments. The ectorhizosphere of Setaria is of particular interest because the plant component of this plant-microbe system is an important agricultural grain crop and a model for biofuel grasses. Importantly, Setaria lends itself to high throughput molecular studies. As such, we have identified important intra- and interspecific microbial and molecular differences in the ectorhizospheres of three geographically distant Setaria italica accessions and their wild ancestor S. viridis. All were grown in a nutrient-poor soil with and without nutrient addition. To assess the contrasting impact of nutrient deficiency observed for two S. italica accessions, we quantitatively evaluated differences in soil organic matter, microbial community, and metabolite profiles. Together, these measurements suggest that rhizosphere priming differs with Setaria accession, which comes from alterations in microbial community abundances, specifically Actinobacteria and Proteobacteria populations. When globally comparing the metabolomic response of Setaria to nutrient addition, plants produced distinctly different metabolic profiles in the leaves and roots. With nutrient addition, increases of nitrogen containing metabolites were significantly higher in plant leaves and roots along with significant increases in tyrosine derived alkaloids, serotonin, and synephrine. Glycerol was also found to be significantly increased in the leaves as well as the ectorhizosphere. These differences provide insight into how C4 grasses adapt to changing nutrient availability in soils or with contrasting fertilization schemas. Gained knowledge could then be utilized in plant enhancement and bioengineering efforts to produce plants with superior traits when grown in nutrient poor soils.

Seawater exposure causes hydraulic damage in dying Sitka-spruce trees.

Sea-level rise is one of the most critical challenges facing coastal ecosystems under climate change. Observations of elevated tree mortality in global coastal forests are increasing, but important knowledge gaps persist concerning the mechanism of salinity stress-induced nonhalophytic tree mortality. We monitored progressive mortality and associated gas exchange and hydraulic shifts in Sitka-spruce (Picea sitchensis) trees located within a salinity gradient under an ecosystem-scale change of seawater exposure in Washington State, USA. Percentage of live foliated crown (PLFC) decreased and tree mortality increased with increasing soil salinity during the study period. A strong reduction in gas exchange and xylem hydraulic conductivity (Ks) occurred during tree death, with an increase in the percentage loss of conductivity (PLC) and turgor loss point (πtlp). Hydraulic and osmotic shifts reflected that hydraulic function declined from seawater exposure, and dying trees were unable to support osmotic adjustment. Constrained gas exchange was strongly related to hydraulic damage at both stem and leaf levels. Significant correlations between foliar sodium (Na+) concentration and gas exchange and key hydraulic parameters (Ks, PLC, and πtlp) suggest that cellular injury related to the toxic effects of ion accumulation impacted the physiology of these dying trees. This study provides evidence of toxic effects on the cellular function that manifests in all aspects of plant functioning, leading to unfavourable osmotic and hydraulic conditions.

Micro on a macroscale: relating microbial-scale soil processes to global ecosystem function.

Soil microorganisms play a key role in driving major biogeochemical cycles and in global responses to climate change. However, understanding and predicting the behavior and function of these microorganisms remains a grand challenge for soil ecology due in part to the microscale complexity of soils. It is becoming increasingly clear that understanding the microbial perspective is vital to accurately predicting global processes. Here, we discuss the microbial perspective including the microbial habitat as it relates to measurement and modeling of ecosystem processes. We argue that clearly defining and quantifying the size, distribution and sphere of influence of microhabitats is crucial to managing microbial activity at the ecosystem scale. This can be achieved using controlled and hierarchical sampling designs. Model microbial systems can provide key data needed to integrate microhabitats into ecosystem models, while adapting soil sampling schemes and statistical methods can allow us to collect microbially-focused data. Quantifying soil processes, like biogeochemical cycles, from a microbial perspective will allow us to more accurately predict soil functions and address long-standing unknowns in soil ecology.

MetFish: a Metabolomics Pipeline for Studying Microbial Communities in Chemically Extreme Environments.

Metabolites have essential roles in microbial communities, including as mediators of nutrient and energy exchange, cell-to-cell communication, and antibiosis. However, detecting and quantifying metabolites and other chemicals in samples having extremes in salt or mineral content using liquid chromatography-mass spectrometry (LC-MS)-based methods remains a significant challenge. Here, we report a facile method based on in situ chemical derivatization followed by extraction for analysis of metabolites and other chemicals in hypersaline samples, enabling for the first time direct LC-MS-based exometabolomics analysis in sample matrices containing up to 2 M total dissolved salts. The method, MetFish, is applicable to molecules containing amine, carboxylic acid, carbonyl, or hydroxyl functional groups, and it can be integrated into either targeted or untargeted analysis pipelines. In targeted analyses, MetFish provided limits of quantification as low as 1 nM, broad linear dynamic ranges (up to 5 to 6 orders of magnitude) with excellent linearity, and low median interday reproducibility (e.g., 2.6%). MetFish was successfully applied in targeted and untargeted exometabolomics analyses of microbial consortia, quantifying amino acid dynamics in the exometabolome during community succession; in situ in a native prairie soil, whose exometabolome was isolated using a hypersaline extraction; and in input and produced fluids from a hydraulically fractured well, identifying dramatic changes in the exometabolome over time in the well. IMPORTANCE The identification and accurate quantification of metabolites using electrospray ionization-mass spectrometry (ESI-MS) in hypersaline samples is a challenge due to matrix effects. Clean-up and desalting strategies that typically work well for samples with lower salt concentrations are often ineffective in hypersaline samples. To address this gap, we developed and demonstrated a simple yet sensitive and accurate method-MetFish-using chemical derivatization to enable mass spectrometry-based metabolomics in a variety of hypersaline samples from varied ecosystems and containing up to 2 M dissolved salts.

Changes in carbon and nitrogen metabolism during seawater-induced mortality of Picea sitchensis trees.

Increasing seawater exposure is causing mortality of coastal forests, yet the physiological response associated with seawater-induced tree mortality, particularly in non-halophytes, is poorly understood. We investigated the shifts in carbon and nitrogen (N) metabolism of mature Sitka-spruce trees that were dying after an ecosystem-scale manipulation of tidal seawater exposure. Soil porewater salinity and foliar ion concentrations increased after seawater exposure and were strongly correlated with the percentage of live foliated crown (PLFC; e.g., crown 'greenness', a measure of progression to death). Co-occurring with decreasing PLFC was decreasing photosynthetic capacity, N-investment into photosynthesis, N-resorption efficiency and non-structural carbohydrate (soluble sugars and starch) concentrations, with the starch reserves depleted to near zero when PLFC dropped below 5%. Combined with declining PLFC, these changes subsequently decreased total carbon gain and thus exacerbated the carbon starvation process. This study suggests that an impairment in carbon and N metabolism during the mortality process after seawater exposure is associated with the process of carbon starvation, and provides critical knowledge necessary to predict sea-level rise impacts on coastal forests.

Declining carbohydrate content of Sitka-spruce treesdying from seawater exposure.

Increasing sea levels associated with climate change threaten the survival of coastal forests, yet the mechanisms by which seawater exposure causes tree death remain poorly understood. Despite the potentially crucial role of nonstructural carbohydrate (NSC) reserves in tree survival, their dynamics in the process of death under seawater exposure are unknown. Here we monitored progressive tree mortality and associated NSC storage in Sitka-spruce (Picea sitchensis) trees dying under ecosystem-scale increases in seawater exposure in western Washington, USA. All trees exposed to seawater, because of monthly tidal intrusion, experienced declining crown foliage during the sampling period, and individuals with a lower percentage of live foliated crown (PLFC) died faster. Tree PLFC was strongly correlated with subsurface salinity and needle ion contents. Total NSC concentrations in trees declined remarkably with crown decline, and reached extremely low levels at tree death (2.4% and 1.6% in leaves and branches, respectively, and 0.4% in stems and roots). Starch in all tissues was almost completely consumed, while sugars remained at a homeostatic level in foliage. The decreasing NSC with closer proximity to death and near zero starch at death are evidences that carbon starvation occurred during Sitka-spruce mortality during seawater exposure. Our results highlight the importance of carbon storage as an indicator of tree mortality risks under seawater exposure.

Microbial Communities Influence Soil Dissolved Organic Carbon Concentration by Altering Metabolite Composition.

Rapid microbial growth in the early phase of plant litter decomposition is viewed as an important component of soil organic matter (SOM) formation. However, the microbial taxa and chemical substrates that correlate with carbon storage are not well resolved. The complexity of microbial communities and diverse substrate chemistries that occur in natural soils make it difficult to identify links between community membership and decomposition processes in the soil environment. To identify potential relationships between microbes, soil organic matter, and their impact on carbon storage, we used sand microcosms to control for external environmental factors such as changes in temperature and moisture as well as the variability in available carbon that exist in soil cores. Using Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) on microcosm samples from early phase litter decomposition, we found that protein- and tannin-like compounds exhibited the strongest correlation to dissolved organic carbon (DOC) concentration. Proteins correlated positively with DOC concentration, while tannins correlated negatively with DOC. Through random forest, neural network, and indicator species analyses, we identified 42 bacterial and 9 fungal taxa associated with DOC concentration. The majority of bacterial taxa (26 out of 42 taxa) belonged to the phylum Proteobacteria while all fungal taxa belonged to the phylum Ascomycota. Additionally, we identified significant connections between microorganisms and protein-like compounds and found that most taxa (12/14) correlated negatively with proteins indicating that microbial consumption of proteins is likely a significant driver of DOC concentration. This research links DOC concentration with microbial production and/or decomposition of specific metabolites to improve our understanding of microbial metabolism and carbon persistence.

Representing the function and sensitivity of coastal interfaces in Earth system models.

Between the land and ocean, diverse coastal ecosystems transform, store, and transport material. Across these interfaces, the dynamic exchange of energy and matter is driven by hydrological and hydrodynamic processes such as river and groundwater discharge, tides, waves, and storms. These dynamics regulate ecosystem functions and Earth's climate, yet global models lack representation of coastal processes and related feedbacks, impeding their predictions of coastal and global responses to change. Here, we assess existing coastal monitoring networks and regional models, existing challenges in these efforts, and recommend a path towards development of global models that more robustly reflect the coastal interface.

Spatial distribution of prokaryotic communities in hypersaline soils.

Increasing salinization in wetland systems is a major threat to ecosystem services carried out by microbial communities. Thus, it is paramount to understand how salinity drives both microbial community structures and their diversity. Here we evaluated the structure and diversity of the prokaryotic communities from a range of highly saline soils (EC1:5 from 5.96 to 61.02 dS/m) from the Odiel Saltmarshes and determined their association with salinity and other soil physicochemical features by analyzing 16S rRNA gene amplicon data through minimum entropy decomposition (MED). We found that these soils harbored unique communities mainly composed of halophilic and halotolerant taxa from the phyla Euryarchaeota, Proteobacteria, Balneolaeota, Bacteroidetes and Rhodothermaeota. In the studied soils, several site-specific properties were correlated with community structure and individual abundances of particular sequence variants. Salinity had a secondary role in shaping prokaryotic communities in these highly saline samples since the dominant organisms residing in them were already well-adapted to a wide range of salinities. We also compared ESV-based results with OTU-clustering derived ones, showing that, in this dataset, no major differences in ecological outcomes were obtained by the employment of one or the other method.

Globally rising soil heterotrophic respiration over recent decades.

Global soils store at least twice as much carbon as Earth's atmosphere1,2. The global soil-to-atmosphere (or total soil respiration, RS) carbon dioxide (CO2) flux is increasing3,4, but the degree to which climate change will stimulate carbon losses from soils as a result of heterotrophic respiration (RH) remains highly uncertain5-8. Here we use an updated global soil respiration database9 to show that the observed soil surface RH:RS ratio increased significantly, from 0.54 to 0.63, between 1990 and 2014 (P = 0.009). Three additional lines of evidence provide support for this finding. By analysing two separate global gross primary production datasets10,11, we find that the ratios of both RH and RS to gross primary production have increased over time. Similarly, significant increases in RH are observed against the longest available solar-induced chlorophyll fluorescence global dataset, as well as gross primary production computed by an ensemble of global land models. We also show that the ratio of night-time net ecosystem exchange to gross primary production is rising across the FLUXNET201512 dataset. All trends are robust to sampling variability in ecosystem type, disturbance, methodology, CO2 fertilization effects and mean climate. Taken together, our findings provide observational evidence that global RH is rising, probably in response to environmental changes, consistent with meta-analyses13-16 and long-term experiments17. This suggests that climate-driven losses of soil carbon are currently occurring across many ecosystems, with a detectable and sustained trend emerging at the global scale.

A moisture function of soil heterotrophic respiration that incorporates microscale processes.

Soil heterotrophic respiration (HR) is an important source of soil-to-atmosphere CO2 flux, but its response to changes in soil water content (θ) is poorly understood. Earth system models commonly use empirical moisture functions to describe the HR-θ relationship, introducing significant uncertainty in predicting CO2 flux from soils. Generalized, mechanistic models that address this uncertainty are thus urgently needed. Here we derive, test, and calibrate a novel moisture function, fm, that encapsulates primary physicochemical and biological processes controlling soil HR. We validated fm using simulation results and published experimental data, and established the quantitative relationships between parameters of fm and measurable soil properties, which enables fm to predict the HR-θ relationships for different soils across spatial scales. The fm function predicted comparable HR-θ relationships with laboratory and field measurements, and may reduce the uncertainty in predicting the response of soil organic carbon stocks to climate change compared with the empirical moisture functions currently used in Earth system models.

Soil carbon cycling proxies: Understanding their critical role in predicting climate change feedbacks.

The complexity of processes and interactions that drive soil C dynamics necessitate the use of proxy variables to represent soil characteristics that cannot be directly measured (correlative proxies), or that aggregate information about multiple soil characteristics into one variable (integrative proxies). These proxies have proven useful for understanding the soil C cycle, which is highly variable in both space and time, and are now being used to make predictions of the fate and persistence of C under future climate scenarios. However, the C pools and processes that proxies represent must be thoughtfully considered in order to minimize uncertainties in empirical understanding. This is necessary to capture the full value of a proxy in model parameters and in model outcomes. Here, we provide specific examples of proxy variables that could improve decision-making, and modeling skill, while also encouraging continued work on their mechanistic underpinnings. We explore the use of three common soil proxies used to study soil C cycling: metabolic quotient, clay content, and physical fractionation. We also consider how emerging data types, such as genome-sequence data, can serve as proxies for microbial community activities. By examining some broad assumptions in soil C cycling with the proxies already in use, we can develop new hypotheses and specify criteria for new and needed proxies.

Shifts in pore connectivity from precipitation versus groundwater rewetting increases soil carbon loss after drought.

Droughts and other extreme precipitation events are predicted to increase in intensity, duration, and extent, with uncertain implications for terrestrial carbon (C) sequestration. Soil wetting from above (precipitation) results in a characteristically different pattern of pore-filling than wetting from below (groundwater), with larger, well-connected pores filling before finer pore spaces, unlike groundwater rise in which capillary forces saturate the finest pores first. Here we demonstrate that pore-scale wetting patterns interact with antecedent soil moisture conditions to alter pore-scale, core-scale, and field-scale C dynamics. Drought legacy and wetting direction are perhaps more important determinants of short-term C mineralization than current soil moisture content in these soils. Our results highlight that microbial access to C is not solely limited by physical protection, but also by drought or wetting-induced shifts in hydrologic connectivity. We argue that models should treat soil moisture within a three-dimensional framework emphasizing hydrologic conduits for C and resource diffusion.

Linking microbial community structure to ß-glucosidic function in soil aggregates.

To link microbial community 16S structure to a measured function in a natural soil, we have scaled both DNA and ß-glucosidase assays down to a volume of soil that may approach a unique microbial community. ß-Glucosidase activity was assayed in 450 individual aggregates, which were then sorted into classes of high or low activities, from which groups of 10 or 11 aggregates were identified and grouped for DNA extraction and pyrosequencing. Tandem assays of ATP were conducted for each aggregate in order to normalize these small groups of aggregates for biomass size. In spite of there being no significant differences in the richness or diversity of the microbial communities associated with high ß-glucosidase activities compared with the communities associated with low ß-glucosidase communities, several analyses of variance clearly show that the communities of these two groups differ. The separation of these groups is partially driven by the differential abundances of members of the Chitinophagaceae family. It may be observed that functional differences in otherwise similar soil aggregates can be largely attributed to differences in resource availability, rather than to the presence or absence of particular taxonomic groups.

Spectral signatures for the classification of microbial species using Raman spectra.

In general, classification-based methods based on confocal Raman microscopy are focused on targeted studies under which the spectral libraries are collected under controlled instrument parameters, which facilitate analyses via standard multivariate data analysis methods and cross-validation. We develop and compare approaches to transform spectra collected at different spectral ranges and varying levels of resolution into a single lower-dimension spectral signature library. This will result in a more robust analysis method able to accommodate spectra accumulated at different times and conditions. We demonstrate these approaches on a relevant test case; the identification of microbial species from a natural environment. The training data were based on samples prepared for three unique species collected at two time points and the test data consisted of blinded unknowns prepared and analyzed at a later date with different instrument parameters. The results indicate that using reduced dimension representations of the spectral signatures improves classification accuracy over basic alignment protocols. In particular, utilizing the microbial species partial least squares discriminant analysis classifier on the blinded samples based on alignment achieved ~78 % accuracy, while both binning and peak selection approaches yielded 100 % accuracy.

Biotic and abiotic reduction and solubilization of Pu(IV)O2•xH2O(am) as affected by anthraquinone-2,6-disulfonate (AQDS) and ethylenediaminetetraacetate (EDTA).

This study measured reductive solubilization of plutonium(IV) hydrous oxide (Pu(IV)O(2)·xH(2)O((am))) with hydrogen (H(2)) as electron donor, in the presence or absence of dissimilatory metal-reducing bacteria (DMRB), anthraquinone-2,6-disulfonate (AQDS), and ethylenediaminetetraacetate (EDTA). In PIPES buffer at pH 7 with excess H(2), Shewanella oneidensis and Geobacter sulfurreducens both solubilized <0.001% of 0.5 mM Pu(IV)O(2)·xH(2)O((am)) over 8 days, with or without AQDS. However, Pu((aq)) increased by an order of magnitude in some treatments, and increases in solubility were associated with production of Pu(III)((aq)). The solid phase of these treatments contained Pu(III)(OH)(3(am)), with more in the DMRB treatments compared with abiotic controls. In the presence of EDTA and AQDS, PuO(2)·xH(2)O((am)) was completely solubilized by S. oneidensis and G. sulfurreducens in ∼24 h. Without AQDS, bioreductive solubilization was slower (∼22 days) and less extensive (∼83-94%). In the absence of DMRB, EDTA facilitated reductive solubilization of 89% (without AQDS) to 98% (with AQDS) of the added PuO(2)·xH(2)O((am)) over 418 days. An in vitro assay demonstrated electron transfer to PuO(2)·xH(2)O((am)) from the S. oneidensis outer-membrane c-type cytochrome MtrC. Our results (1) suggest that PuO(2)·xH(2)O((am)) reductive solubilization may be important in reducing environments, especially in the presence of complexing ligands and electron shuttles, (2) highlight the environmental importance of polynuclear, colloidal Pu, (3) provide additional evidence that Pu(III)-EDTA is a more likely mobile form of Pu than Pu(IV)-EDTA, and (4) provide another example of outer-membrane cytochromes and electron-shuttling compounds facilitating bioreduction of insoluble electron acceptors in geologic environments.

Phospholipid fatty acid biomarkers in a freshwater periphyton community exposed to uranium: discovery by non-linear statistical learning.

Phospholipid fatty acids (PLFA) have been widely used to characterize environmental microbial communities, generating community profiles that can distinguish phylogenetic or functional groups within the community. The poor specificity of organism groups with fatty acid biomarkers in the classic PLFA-microorganism associations is a confounding factor in many of the statistical classification/clustering approaches traditionally used to interpret PLFA profiles. In this paper we demonstrate that non-linear statistical learning methods, such as a support vector machine (SVM), can more accurately find patterns related to uranyl nitrate exposure in a freshwater periphyton community than linear methods, such as partial least squares discriminant analysis. In addition, probabilistic models of exposure can be derived from the identified lipid biomarkers to demonstrate the potential model-based approach that could be used in remediation. The SVM probability model separates dose groups at accuracies of ∼87.0%, ∼71.4%, ∼87.5%, and 100% for the four groups; Control (non-amended system), low dose (amended at 10 µg UL⁻¹), medium dose (amended at 100 µg UL⁻¹), and high dose (500 µg UL⁻¹). The SVM model achieved an overall cross-validated classification accuracy of ∼87% in contrast to ∼59% for the best linear classifier.

Novelty and uniqueness patterns of rare members of the soil biosphere.

Soil bacterial communities typically exhibit a distribution pattern in which most bacterial species are present in low abundance. Due to the relatively small size of most culture-independent sequencing surveys, a detailed phylogenetic analysis of rare members of the community is lacking. To gain access to the rarely sampled soil biosphere, we analyzed a data set of 13,001 near-full-length 16S rRNA gene clones derived from an undisturbed tall grass prairie soil in central Oklahoma. Rare members of the soil bacterial community (empirically defined at two different abundance cutoffs) represented 18.1 to 37.1% of the total number of clones in the data set and were, on average, less similar to their closest relatives in public databases when compared to more abundant members of the community. Detailed phylogenetic analyses indicated that members of the soil rare biosphere either belonged to novel bacterial lineages (members of five novel bacterial phyla identified in the data set, as well as members of multiple novel lineages within previously described phyla or candidate phyla), to lineages that are prevalent in other environments but rarely encountered in soil, or were close relatives to more abundant taxa in the data set. While a fraction of the rare community was closely related to more abundant taxonomic groups in the data set, a significant portion of the rare biosphere represented evolutionarily distinct lineages at various taxonomic cutoffs. We reason that these novelty and uniqueness patterns provide clues regarding the origins and potential ecological roles of members of the soil's rare biosphere.