The gut microbiome is widely studied by fecal sampling, but the extent to which stool reflects the commensal composition at intestinal sites is poorly understood. We investigated this relationship in ...rhesus macaques by 16S sequencing feces and paired lumenal and mucosal samples from ten sites distal to the jejunum. Stool composition correlated highly with the colonic lumen and mucosa and moderately with the distal small intestine. The mucosal microbiota varied most based on location and was enriched in oxygen-tolerant taxa (e.g., Helicobacter and Treponema), while the lumenal microbiota showed inter-individual variation and obligate anaerobe enrichment (e.g., Firmicutes). This mucosal and lumenal community variability corresponded to functional differences, such as nutrient availability. Additionally, Helicobacter, Faecalibacterium, and Lactobacillus levels in stool were highly predictive of their abundance at most other gut sites. These results quantify the composition and biogeographic relationships between gut microbial communities in macaques and support fecal sampling for translational studies.
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•Macaque stool microbiota is highly representative of the colonic lumen and mucosa•Small intestinal proteobacteria are particularly under-detected in stool•Facultative and obligate anaerobes are respectively enriched in the mucosa and lumen
Stool is commonly used for microbiota assessments, but how accurately it represents the gut is unknown. Yasuda et al. characterize biogeographic relationships in the rhesus macaque intestinal microbiome and find that stool is highly representative of the colonic lumen and mucosa, which are respectively enriched in obligate and facultative anaerobes.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This study quantitatively estimates the spatial distribution of anthropogenic methane sources in the United States by combining comprehensive atmospheric methane observations, extensive spatial ...datasets, and a high-resolution atmospheric transport model. Results show that current inventories from the US Environmental Protection Agency (EPA) and the Emissions Database for Global Atmospheric Research underestimate methane emissions nationally by a factor of ∼1.5 and ∼1.7, respectively. Our study indicates that emissions due to ruminants and manure are up to twice the magnitude of existing inventories. In addition, the discrepancy in methane source estimates is particularly pronounced in the south-central United States, where we find total emissions are ∼2.7 times greater than in most inventories and account for 24 ± 3% of national emissions. The spatial patterns of our emission fluxes and observed methane–propane correlations indicate that fossil fuel extraction and refining are major contributors (45 ± 13%) in the south-central United States. This result suggests that regional methane emissions due to fossil fuel extraction and processing could be 4.9 ± 2.6 times larger than in EDGAR, the most comprehensive global methane inventory. These results cast doubt on the US EPA’s recent decision to downscale its estimate of national natural gas emissions by 25–30%. Overall, we conclude that methane emissions associated with both the animal husbandry and fossil fuel industries have larger greenhouse gas impacts than indicated by existing inventories.
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Singlet fission (SF) in polycrystalline thin films of four 3,6-bis(thiophen-2-yl)diketopyrrolopyrrole (TDPP) chromophores with methyl (Me), n-hexyl (C6), triethylene glycol (TEG), and 2-ethylhexyl ...(EH) substituents at the 2,5-positions is found to involve an intermediate excimer-like state. The four different substituents yield four distinct intermolecular packing geometries, resulting in variable intermolecular charge transfer (CT) interactions in the solid. SF from the excimer state of Me, C6, TEG, and EH takes place in τSF = 22, 336, 195, and 1200 ps, respectively, to give triplet yields of 200%, 110%, 110%, and 70%, respectively. The transient spectra of the excimer-like state and its energetic proximity to the lowest excited singlet state in these derivatives suggests that this state may be the multiexciton 1(T1T1) state that precedes formation of the uncorrelated triplet excitons. The excimer decay rates correlate well with the SF efficiencies and the degree of intermolecular donor–acceptor interactions resulting from π-stacking of the thiophene donor of one molecule with the DPP core acceptor in another molecule as observed in the crystal structures. Such interactions are found to also increase with the SF coupling energies, as calculated for each derivative. These structural and spectroscopic studies afford a better understanding of the electronic interactions that enhance SF in chromophores having strong intra- and intermolecular CT character.
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Singlet exciton fission (SF) in organic chromophore assemblies results in the conversion of one singlet exciton (S1) into two triplet excitons (T1), provided that the overall process is exoergic, ...i.e., E(S1) > 2E(T1). We report on SF in thin polycrystalline films of two terrylene-3,4:11,12-bis(dicarboximide) (TDI) derivatives 1 and 2, which crystallize into two distinct π-stacked structures. Femtosecond transient absorption spectroscopy (fsTA) reveals a charge-transfer state preceding a 190% T1 yield in films of 1, where the π-stacked TDI molecules are rotated by 23° along an axis perpendicular to their π systems. In contrast, when the TDI molecules are slip-stacked along their N–N axes in films of 2, fsTA shows excimer formation, followed by a 50% T1 yield.
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Overwintering fires in boreal forests Scholten, Rebecca C; Jandt, Randi; Miller, Eric A ...
Nature (London),
05/2021, Volume:
593, Issue:
7859
Journal Article
Peer reviewed
Open access
Forest fires are usually viewed within the context of a single fire season, in which weather conditions and fuel supply can combine to create conditions favourable for fire ignition-usually by ...lightning or human activity-and spread
. But some fires exhibit 'overwintering' behaviour, in which they smoulder through the non-fire season and flare up in the subsequent spring
. In boreal (northern) forests, deep organic soils favourable for smouldering
, along with accelerated climate warming
, may present unusually favourable conditions for overwintering. However, the extent of overwintering in boreal forests and the underlying factors influencing this behaviour remain unclear. Here we show that overwintering fires in boreal forests are associated with hot summers generating large fire years and deep burning into organic soils, conditions that have become more frequent in our study areas in recent decades. Our results are based on an algorithm with which we detect overwintering fires in Alaska, USA, and the Northwest Territories, Canada, using field and remote sensing datasets. Between 2002 and 2018, overwintering fires were responsible for 0.8 per cent of the total burned area; however, in one year this amounted to 38 per cent. The spatiotemporal predictability of overwintering fires could be used by fire management agencies to facilitate early detection, which may result in reduced carbon emissions and firefighting costs.
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GEOZS, IJS, IMTLJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZAGLJ
Motion‐activated cameras (“camera traps”) are increasingly used in ecological and management studies for remotely observing wildlife and are amongst the most powerful tools for wildlife research. ...However, studies involving camera traps result in millions of images that need to be analysed, typically by visually observing each image, in order to extract data that can be used in ecological analyses.
We trained machine learning models using convolutional neural networks with the ResNet‐18 architecture and 3,367,383 images to automatically classify wildlife species from camera trap images obtained from five states across the United States. We tested our model on an independent subset of images not seen during training from the United States and on an out‐of‐sample (or “out‐of‐distribution” in the machine learning literature) dataset of ungulate images from Canada. We also tested the ability of our model to distinguish empty images from those with animals in another out‐of‐sample dataset from Tanzania, containing a faunal community that was novel to the model.
The trained model classified approximately 2,000 images per minute on a laptop computer with 16 gigabytes of RAM. The trained model achieved 98% accuracy at identifying species in the United States, the highest accuracy of such a model to date. Out‐of‐sample validation from Canada achieved 82% accuracy and correctly identified 94% of images containing an animal in the dataset from Tanzania. We provide an r package (Machine Learning for Wildlife Image Classification) that allows the users to (a) use the trained model presented here and (b) train their own model using classified images of wildlife from their studies.
The use of machine learning to rapidly and accurately classify wildlife in camera trap images can facilitate non‐invasive sampling designs in ecological studies by reducing the burden of manually analysing images. Our r package makes these methods accessible to ecologists.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
The water–gas shift (WGS) reaction rate per total mole of Au under 7% CO, 8.5% CO2, 22% H2O, and 37% H2 at 1 atm for Au/Al2O3 catalysts at 180 °C and Au/TiO2 catalysts at 120 °C varies with the ...number average Au particle size (d) as d –2.2±0.2 and d –2.7±0.1, respectively. The use of nonporous and crystalline, model Al2O3 and TiO2 supports allowed the imaging of the active catalyst and enabled a precise determination of the Au particle size distribution and particle shape using transmission electron microscopy (TEM). Further, the apparent reaction orders and the stretching frequency of CO adsorbed on Au0 (near 2100 cm–1) determined by diffuse reflectance infrared spectroscopy (DRIFTS) depend on d. Because of the changes in reaction rates, kinetics, and the CO stretching frequency with number average Au particle size, it is determined that the dominant active sites are the low coordinated corner Au sites, which are 3 and 7 times more active than the perimeter Au sites for Au/Al2O3 and Au/TiO2 catalysts, respectively, and 10 times more active for Au on TiO2 versus Al2O3. From operando Fourier transform infrared spectroscopy (FTIR) experiments, it is determined that the active Au sites are metallic in nature. In addition, Au/Al2O3 catalysts have a higher apparent H2O order (0.63) and lower apparent activation energy (9 kJ mol–1) than Au/TiO2 catalysts with apparent H2O order of −0.42 to −0.21 and activation energy of 45–60 kJ mol–1 at near 120 °C. From these data, we conclude that the support directly participates by activating H2O molecules.
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Urban areas now house more than half the world's population, and are estimated to contribute over 70% of global energy‐related CO2emissions. Many cities have emission reduction policies in place, but ...lack objective, observation‐based methods for verifying their outcomes. Here we demonstrate the potential of satellite‐borne instruments to provide accurate global monitoring of megacity CO2 emissions using GOSAT observations of column averaged CO2 dry air mole fraction (XCO2) collected over Los Angeles and Mumbai. By differencing observations over the megacity with those in nearby background, we observe robust, statistically significant XCO2 enhancements of 3.2 ± 1.5 ppm for Los Angeles and 2.4 ± 1.2 ppm for Mumbai, and find these enhancements can be exploited to track anthropogenic emission trends over time. We estimate that XCO2 changes as small as 0.7 ppm in Los Angeles, corresponding to a 22% change in emissions, could be detected with GOSAT at the 95% confidence level.
Key Points
Existing satellite observations can detect megacity CO2 enhancements
These observations can be used to track anthropogenic emissions trends in time
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Fungal pathogens, among other stressors, negatively impact the productivity and population size of honey bees, one of our most important pollinators (1, 2), in particular their brood (larvae and ...pupae) (3, 4). Understanding the factors that influence disease incidence and prevalence in brood may help us improve colony health and productivity. Here, we examined the capacity of a honey bee-associated bacterium, Bombella apis, to suppress the growth of fungal pathogens and ultimately protect bee brood from infection. Our results showed that strains of
inhibit the growth of two insect fungal pathogens, Beauveria bassiana and Aspergillus flavus,
. This phenotype was recapitulated
; bee broods supplemented with
were significantly less likely to be infected by A. flavus. Additionally, the presence of
reduced sporulation of A. flavus in the few bees that were infected. Analyses of biosynthetic gene clusters across
strains suggest antifungal candidates, including a type 1 polyketide, terpene, and aryl polyene. Secreted metabolites from
alone were sufficient to suppress fungal growth, supporting the hypothesis that fungal inhibition is mediated by an antifungal metabolite. Together, these data suggest that
can suppress fungal infections in bee brood via secretion of an antifungal metabolite.
Fungi can play critical roles in host microbiomes (5-7), yet bacterial-fungal interactions are understudied. For insects, fungi are the leading cause of disease (5, 8). In particular, populations of the European honey bee (Apis mellifera), an agriculturally and economically critical species, have declined in part due to fungal pathogens. The presence and prevalence of fungal pathogens in honey bees have far-reaching consequences, endangering other species and threatening food security (1, 2, 9). Our research highlights how a bacterial symbiont protects bee brood from fungal infection. Further mechanistic work could lead to the development of new antifungal treatments.
Summary
Our understanding of a biological population can be greatly enhanced by modelling their distribution in space and as a function of environmental covariates. Such models can be used to ...investigate the relationships between distribution and environmental covariates as well as reliably estimate abundances and create maps of animal/plant distribution.
Density surface models consist of a spatial model of the abundance of a biological population which has been corrected for uncertain detection via distance sampling methods.
We review recent developments in the field and consider the likely directions of future research before focussing on a popular approach based on generalized additive models. In particular, we consider spatial modelling techniques that may be advantageous to applied ecologists such as quantification of uncertainty in a two‐stage model and smoothing in areas with complex boundaries.
The methods discussed are available in an R package developed by the authors (dsm) and are largely implemented in the popular Windows software Distance.
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