A functional microbiome catalog crowdsourced from North American rivers.

Predicting elemental cycles and maintaining water quality under increasing anthropogenic influence requires understanding the spatial drivers of river microbiomes. However, the unifying microbial processes governing river biogeochemistry are hindered by a lack of genome-resolved functional insights and sampling across multiple rivers. Here we employed a community science effort to accelerate the sampling, sequencing, and genome-resolved analyses of river microbiomes to create the Genome Resolved Open Watersheds database (GROWdb). This resource profiled the identity, distribution, function, and expression of thousands of microbial genomes across rivers covering 90% of United States watersheds. Specifically, GROWdb encompasses 1,469 microbial species from 27 phyla, including novel lineages from 10 families and 128 genera, and defines the core river microbiome for the first time at genome level. GROWdb analyses coupled to extensive geospatial information revealed local and regional drivers of microbial community structuring, while also presenting a myriad of foundational hypotheses about ecosystem function. Building upon the previously conceived River Continuum Concept 1 , we layer on microbial functional trait expression, which suggests the structure and function of river microbiomes is predictable. We make GROWdb available through various collaborative cyberinfrastructures 2, 3 so that it can be widely accessed across disciplines for watershed predictive modeling and microbiome-based management practices.

The extent to which soil hydraulics can explain ecohydrological separation.

Field measurements of hydrologic tracers indicate varying magnitudes of geochemical separation between subsurface pore waters. The potential for conventional soil physics alone to explain isotopic differences between preferential flow and tightly-bound water remains unclear. Here, we explore physical drivers of isotopic separations using 650 different model configurations of soil, climate, and mobile/immobile soil-water domain characteristics, without confounding fractionation or plant uptake effects. We find simulations with coarser soils and less precipitation led to reduced separation between pore spaces and drainage. Amplified separations are found with larger immobile domains and, to a lesser extent, higher mobile-immobile transfer rates. Nonetheless, isotopic separations remained small (<4‰ for δ2H) across simulations, indicating that contrasting transport dynamics generate limited geochemical differences. Therefore, conventional soil physics alone are unlikely to explain large ecohydrological separations observed elsewhere, and further efforts aimed at reducing methodological artifacts, refining understanding of fractionation processes, and investigating new physiochemical mechanisms are needed.

The streamwater microbiome encodes hydrologic data across scales.

Many fundamental questions in hydrology remain unanswered due to the limited information that can be extracted from existing data sources. Microbial communities constitute a novel type of environmental data, as they are comprised of many thousands of taxonomically and functionally diverse groups known to respond to both biotic and abiotic environmental factors. As such, these microscale communities reflect a range of macroscale conditions and characteristics, some of which also drive hydrologic regimes. Here, we assess the extent to which streamwater microbial communities (as characterized by 16S gene amplicon sequence abundance) encode information about catchment hydrology across scales. We analyzed 64 summer streamwater DNA samples collected from subcatchments within the Willamette, Deschutes, and John Day river basins in Oregon, USA, which range 0.03-29,000 km2 in area and 343-2334 mm/year of precipitation. We applied information theory to quantify the breadth and depth of information about common hydrologic metrics encoded within microbial taxa. Of the 256 microbial taxa that spanned all three watersheds, we found 9.6 % (24.5/256) of taxa, on average, shared information with a given hydrologic metric, with a median 15.6 % (range = 12.4-49.2 %) reduction in uncertainty of that metric based on knowledge of the microbial biogeography. All of the hydrologic metrics we assessed, including daily discharge at different time lags, mean monthly discharge, and seasonal high and low flow durations were encoded within the microbial community. Summer microbial taxa shared the most information with winter mean flows. Our study demonstrates quantifiable relationships between streamwater microbial taxa and hydrologic metrics at different scales, likely resulting from the integration of multiple overlapping drivers of each. Streamwater microbial communities are rich sources of information that may contribute fresh insight to unresolved hydrologic questions.

The NEON Daily Isotopic Composition of Environmental Exchanges Dataset.

The National Ecological Observatory Network (NEON) provides open-access measurements of stable isotope ratios in atmospheric water vapor (δ2H, δ18O) and carbon dioxide (δ13C) at different tower heights, as well as aggregated biweekly precipitation samples (δ2H, δ18O) across the United States. These measurements were used to create the NEON Daily Isotopic Composition of Environmental Exchanges (NEON-DICEE) dataset estimating precipitation (P; δ2H, δ18O), evapotranspiration (ET; δ2H, δ18O), and net ecosystem exchange (NEE; δ13C) isotope ratios. Statistically downscaled precipitation datasets were generated to be consistent with the estimated covariance between isotope ratios and precipitation amounts at daily time scales. Isotope ratios in ET and NEE fluxes were estimated using a mixing-model approach with calibrated NEON tower measurements. NEON-DICEE is publicly available on HydroShare and can be reproduced or modified to fit user specific applications or include additional NEON data records as they become available. The NEON-DICEE dataset can facilitate understanding of terrestrial ecosystem processes through their incorporation into environmental investigations that require daily δ2H, δ18O, and δ13C flux data.

Machine Learning Applications for Chemical Fingerprinting and Environmental Source Tracking Using Non-target Chemical Data.

A frequent goal of chemical forensic analyses is to select a panel of diagnostic chemical features─colloquially termed a chemical fingerprint─that can predict the presence of a source in a novel sample. However, most of the developed chemical fingerprinting workflows are qualitative in nature. Herein, we report on a quantitative machine learning workflow. Grab samples (n = 51) were collected from five chemical sources, including agricultural runoff, headwaters, livestock manure, (sub)urban runoff, and municipal wastewater. Support vector classification was used to select the top 10, 25, 50, and 100 chemical features that best discriminate each source from all others. The cross-validation balanced accuracy was 92-100% for all sources (n = 1,000 iterations). When screening for diagnostic features from each source in samples collected from four local creeks, presence probabilities were low for all sources, except for wastewater at two downstream locations in a single creek. Upon closer investigation, a wastewater treatment facility was located ∼3 km upstream of the nearest sample location. In addition, using simulated in silico mixtures, the workflow can distinguish presence and absence of some sources at 10,000-fold dilutions. These results strongly suggest that this workflow can select diagnostic subsets of chemical features that can be used to quantitatively predict the presence/absence of various sources at trace levels in the environment.

Comment on "The global tree restoration potential".

Bastin et al's estimate (Reports, 5 July 2019, p. 76) that tree planting for climate change mitigation could sequester 205 gigatonnes of carbon is approximately five times too large. Their analysis inflated soil organic carbon gains, failed to safeguard against warming from trees at high latitudes and elevations, and considered afforestation of savannas, grasslands, and shrublands to be restoration.

Solar PV Power Potential is Greatest Over Croplands.

Solar energy has the potential to offset a significant fraction of non-renewable electricity demands globally, yet it may occupy extensive areas when deployed at this level. There is growing concern that large renewable energy installations will displace other land uses. Where should future solar power installations be placed to achieve the highest energy production and best use the limited land resource? The premise of this work is that the solar panel efficiency is a function of the location's microclimate within which it is immersed. Current studies largely ignore many of the environmental factors that influence Photovoltaic (PV) panel function. A model for solar panel efficiency that incorporates the influence of the panel's microclimate was derived from first principles and validated with field observations. Results confirm that the PV panel efficiency is influenced by the insolation, air temperature, wind speed and relative humidity. The model was applied globally using bias-corrected reanalysis datasets to map solar panel efficiency and the potential for solar power production given local conditions. Solar power production potential was classified based on local land cover classification, with croplands having the greatest median solar potential of approximately 28 W/m2. The potential for dual-use, agrivoltaic systems may alleviate land competition or other spatial constraints for solar power development, creating a significant opportunity for future energy sustainability. Global energy demand would be offset by solar production if even less than 1% of cropland were converted to an agrivoltaic system.

Inferring the source of evaporated waters using stable H and O isotopes.

Stable isotope ratios of H and O are widely used to identify the source of water, e.g., in aquifers, river runoff, soils, plant xylem, and plant-based beverages. In situations where the sampled water is partially evaporated, its isotope values will have evolved along an evaporation line (EL) in δ2H/δ18O space, and back-correction along the EL to its intersection with a meteoric water line (MWL) has been used to estimate the source water's isotope ratios. Here, we review the theory underlying isotopic estimation of source water for evaporated samples (iSWE). We note potential for bias from a commonly used regression-based approach for EL slope estimation and suggest that a model-based approach may be preferable if assumptions of the regression approach are not valid. We then introduce a mathematical framework that eliminates the need to explicitly estimate the EL-MWL intersection, simplifying iSWE analysis and facilitating more rigorous uncertainty estimation. We apply this approach to data from the US EPA's 2007 National Lakes Assessment. We find that data for most lakes are consistent with a water source similar to annual runoff, estimated from monthly precipitation and evaporation within the lake basin. Strong evidence for both summer- and winter-biased sources exists, however, with winter bias pervasive in most snow-prone regions. The new analytical framework should improve the rigor of iSWE in ecohydrology and related sciences, and our initial results from US lakes suggest that previous interpretations of lakes as unbiased isotope integrators may only be valid in certain climate regimes.

Comment on "The extent of forest in dryland biomes".

Bastin et al (Reports, 12 May 2017, p. 635) infer forest as more globally extensive than previously estimated using tree cover data. However, their forest definition does not reflect ecosystem function or biotic composition. These structural and climatic definitions inflate forest estimates across the tropics and undermine conservation goals, leading to inappropriate management policies and practices in tropical grassy ecosystems.

A mesic maximum in biological water use demarcates biome sensitivity to aridity shifts.

Biome function is largely governed by how efficiently available resources can be used and yet for water, the ratio of direct biological resource use (transpiration, E T) to total supply (annual precipitation, P) at ecosystem scales remains poorly characterized. Here, we synthesize field, remote sensing and ecohydrological modelling estimates to show that the biological water use fraction (E T/P) reaches a maximum under mesic conditions; that is, when evaporative demand (potential evapotranspiration, E P) slightly exceeds supplied precipitation. We estimate that this mesic maximum in E T/P occurs at an aridity index (defined as E P/P) between 1.3 and 1.9. The observed global average aridity of 1.8 falls within this range, suggesting that the biosphere is, on average, configured to transpire the largest possible fraction of global precipitation for the current climate. A unimodal E T/P distribution indicates that both dry regions subjected to increasing aridity and humid regions subjected to decreasing aridity will suffer declines in the fraction of precipitation that plants transpire for growth and metabolism. Given the uncertainties in the prediction of future biogeography, this framework provides a clear and concise determination of ecosystems' sensitivity to climatic shifts, as well as expected patterns in the amount of precipitation that ecosystems can effectively use.

WATER RESOURCES. Hydrologic connectivity constrains partitioning of global terrestrial water fluxes.

Continental precipitation not routed to the oceans as runoff returns to the atmosphere as evapotranspiration. Partitioning this evapotranspiration flux into interception, transpiration, soil evaporation, and surface water evaporation is difficult using traditional hydrological methods, yet critical for understanding the water cycle and linked ecological processes. We combined two large-scale flux-partitioning approaches to quantify evapotranspiration subcomponents and the hydrologic connectivity of bound, plant-available soil waters with more mobile surface waters. Globally, transpiration is 64 ± 13% (mean ± 1 standard deviation) of evapotranspiration, and 65 ± 26% of evaporation originates from soils and not surface waters. We estimate that 38 ± 28% of surface water is derived from the plant-accessed soil water pool. This limited connectivity between soil and surface waters fundamentally structures the physical and biogeochemical interactions of water transiting through catchments.

Vapor hydrogen and oxygen isotopes reflect water of combustion in the urban atmosphere.

Anthropogenic modification of the water cycle involves a diversity of processes, many of which have been studied intensively using models and observations. Effective tools for measuring the contribution and fate of combustion-derived water vapor in the atmosphere are lacking, however, and this flux has received relatively little attention. We provide theoretical estimates and a first set of measurements demonstrating that water of combustion is characterized by a distinctive combination of H and O isotope ratios. We show that during periods of relatively low humidity and/or atmospheric stagnation, this isotopic signature can be used to quantify the concentration of water of combustion in the atmospheric boundary layer over Salt Lake City. Combustion-derived vapor concentrations vary between periods of atmospheric stratification and mixing, both on multiday and diurnal timescales, and respond over periods of hours to variations in surface emissions. Our estimates suggest that up to 13% of the boundary layer vapor during the period of study was derived from combustion sources, and both the temporal pattern and magnitude of this contribution were closely reproduced by an independent atmospheric model forced with a fossil fuel emissions data product. Our findings suggest potential for water vapor isotope ratio measurements to be used in conjunction with other tracers to refine the apportionment of urban emissions, and imply that water vapor emissions associated with combustion may be a significant component of the water budget of the urban boundary layer, with potential implications for urban climate, ecohydrology, and photochemistry.

Stable isotope analysis of precipitation samples obtained via crowdsourcing reveals the spatiotemporal evolution of Superstorm Sandy.

Extra-tropical cyclones, such as 2012 Superstorm Sandy, pose a significant climatic threat to the northeastern United Sates, yet prediction of hydrologic and thermodynamic processes within such systems is complicated by their interaction with mid-latitude water patterns as they move poleward. Fortunately, the evolution of these systems is also recorded in the stable isotope ratios of storm-associated precipitation and water vapor, and isotopic analysis provides constraints on difficult-to-observe cyclone dynamics. During Superstorm Sandy, a unique crowdsourced approach enabled 685 precipitation samples to be obtained for oxygen and hydrogen isotopic analysis, constituting the largest isotopic sampling of a synoptic-scale system to date. Isotopically, these waters span an enormous range of values (> 21‰ for δ(18)O, > 160‰ for δ(2)H) and exhibit strong spatiotemporal structure. Low isotope ratios occurred predominantly in the west and south quadrants of the storm, indicating robust isotopic distillation that tracked the intensity of the storm's warm core. Elevated values of deuterium-excess (> 25‰) were found primarily in the New England region after Sandy made landfall. Isotope mass balance calculations and Lagrangian back-trajectory analysis suggest that these samples reflect the moistening of dry continental air entrained from a mid-latitude trough. These results demonstrate the power of rapid-response isotope monitoring to elucidate the structure and dynamics of water cycling within synoptic-scale systems and improve our understanding of storm evolution, hydroclimatological impacts, and paleo-storm proxies.

Climatological determinants of woody cover in Africa.

Determining the factors that influence the distribution of woody vegetation cover and resolving the sensitivity of woody vegetation cover to shifts in environmental forcing are critical steps necessary to predict continental-scale responses of dryland ecosystems to climate change. We use a 6-year satellite data record of fractional woody vegetation cover and an 11-year daily precipitation record to investigate the climatological controls on woody vegetation cover across the African continent. We find that-as opposed to a relationship with only mean annual rainfall-the upper limit of fractional woody vegetation cover is strongly influenced by both the quantity and intensity of rainfall events. Using a set of statistics derived from the seasonal distribution of rainfall, we show that areas with similar seasonal rainfall totals have higher fractional woody cover if the local rainfall climatology consists of frequent, less intense precipitation events. Based on these observations, we develop a generalized response surface between rainfall climatology and maximum woody vegetation cover across the African continent. The normalized local gradient of this response surface is used as an estimator of ecosystem vegetation sensitivity to climatological variation. A comparison between predicted climate sensitivity patterns and observed shifts in both rainfall and vegetation during 2009 reveals both the importance of rainfall climatology in governing how ecosystems respond to interannual fluctuations in climate and the utility of our framework as a means to forecast continental-scale patterns of vegetation shifts in response to future climate change.