Comprehensive characterization of microbiota in the gastrointestinal tract of quails and two high yielding laying hen breeds

The microbiomes composition in the gastrointestinal tract (GIT) is subject to several changes and influences. In addition to breed, sex, or diet, age affects the GIT microbiome dynamics of laying hens and quails. From the first day, the microbiome develops and increases its bacterial load to thousands of species. Then, depending on the diet fed, the animals microbiome and associated active bacteria vary and directly influence the animals nutrient uptake and efficiency. Omics technologies give insights into changes in microbes in the GIT (crop, gizzard, duodenum, ileum, caeca). In addition, they can reveal how feed supplements such as calcium (Ca) or phosphorus (P) can affect host health and performance through alterations in the microbiome. The Japanese quail has been an established animal model for nutritional and biological studies in poultry for the last 60 years. In particular, its short development time makes it a convenient model for microbiome research. However, compared to broiler microbiome research, the quail microbiome is still poorly understood. Animals of the breed Coturnix japonica were housed under the same conditions, fed a diet with P below recommendation, and the ileum microbiota characterized. Microbiota relations with gender and higher or lower predisposition of the birds for PU, CaU, FI, BWG, and FC were described (Chapter II). In addition, these performance parameters influenced the relative average abundance of bacteria like Candidatus Arthromitus, Bacillus, and Leuconostoc. Gender affects specific bacterial groups of the GIT, such as Lactobacillus, Streptococcus, Escherichia, and Clostridium, which differ in average abundance between male and female quails. Despite the comprehensive microbiota analysis, the interplay between animal genetics, diet, sex, and microbiome functionality is not yet understood. The laying hen breeds Lohmann LSL-Classic and Lohmann Brown-Classic are used worldwide. Little is known about the interaction with microbiome composition, performance, dietary effects, and changes during the productive life that might help develop feeding strategies and microbiome responses on a large scale. Because of the importance of P and Ca in poultry diet, the research in Chapter III was conducted to challenge laying hens with reduced dietary P and Ca and describe the effect on GIT active microbiota. The breed was the primary driver of microbial differences. A core microbiome of active bacteria, present along the complete GIT, was revealed for the first time and consisted of five bacteria detected in 97% of all samples, including digesta and mucosa samples (uncl. Lactobacillus, Megamonas funiformis, Ligilactobacillus salivarius, Lactobacillus helveticus, uncl. Fuscatenibacter). Furthermore, significant microbial differences between the GIT sections and between the breeds were described. Minor dietary effects of the P and Ca reduction on the microbiota showed that a further decrease in Ca and P supplementation might be possible without affecting the gut microbial composition and bird performance. Furthermore, the microbiome of laying hens was characterized at five productive stages (weeks 10, 16, 24, 30, and 60) to analyze the age effect on the GIT microbiome (Chapter IV). Although the two breeds of laying hens were offered the same diet and housed under similar conditions, the active microbiota composition changed between the analyzed productive stages, the breed and the GIT sections. The major shift occurred between weeks 16 and 24 and supported the hypothesis of bacterial fluctuations due to the onset of the laying period. Those changes occurred mainly in the abundance of the genera Lactobacillus and Ligilactobacillus. However, it remains unclear whether the dietary changes, due to the development of the birds, influenced the microbiota shifts or if the anatomical and physiological modifications influenced the GIT microbiota. Furthermore, the shotgun metagenomic analysis revealed differences in regulatory functions and pathways between breeds, sections, and the two production stages. Different relative abundance levels of the microbial composition were observed between the RNA-based targeted sequencing and the DNA-based shotgun metagenomics. In conclusion, the comprehensive characterization of the microbiota in the GIT of quails and two high-yielding breeds of laying hens contributes to a broader knowledge of the microbiome dynamics within the fowl GIT. Age and breed play a more important role than diet in influencing the dynamics of microbial composition in laying hens, and individual performance and sex in quails. Research characterizing the microbiome in poultry and its effect on diet and host genetics will help improve feeding and breeding strategies in the future and reduce excretion of nutrients into the environment while ensuring overall animal health.Die Zusammensetzung des Mikrobioms im Gastrointestinaltrakt (GIT) unterliegt verschiedenen Veränderungen und Einflüssen. Neben Rasse, Linie, Geschlecht oder Ernährung wirkt sich auch das Alter auf die Dynamik des GIT-Mikrobioms von Legehennen und Wachteln aus. Vom ersten Tag an entwickelt sich das Mikrobiom und erhöht seine bakterielle Besiedelung auf Tausende von Arten. Desweiteren variiert das Mikrobiom des Tieres und die damit verbundenen aktiven Bakterien je nach der gefütterten Nahrung und beeinflussen direkt die Nährstoffaufnahme und Effizienz des Tieres. Omics-Technologien geben Aufschluss über Veränderungen der Mikroben im GIT. Darüber hinaus können sie aufzeigen, wie sich Futterzusätze wie Kalzium (Ca) oder Phosphor (P) durch Veränderungen im Mikrobiom auf die Gesundheit und Leistung des Wirts auswirken können. Die japanische Wachtel ist seit 60 Jahren ein etabliertes Modelltier für ernährungswissenschaftliche und biologische Studien an Geflügel. Im Vergleich zur Mikrobiomforschung bei Masthähnchen ist das Mikrobiom der Wachtel jedoch wenig erforscht. Daher wurde die Microbiota des Ileums von Tieren der Rasse Coturnix japonica, welche unter identischen Bedingungen gehalten wurden, charakterisiert, wobei der Phosphorgehalt unter der allgemeinen Empfehlung lag. Es wurden Beziehungen zwischen der GIT Mikrobiota und dem Geschlecht sowie einer höheren oder niedrigeren Prädisposition der Tiere für P -verwertung, Ca -verwertung, Futteraufnahme, Körpergewichtszunahme und Futterverwertung beschrieben. Darüber hinaus beeinflussten diese Leistungsparameter die relative durchschnittliche Abundanz von Bakterien wie Candidatus Arthromitus, Bacillus und Leuconostoc. Das Geschlecht wirkt sich auf Bakterien des GIT aus, wie z. B. Lactobacillus, Streptococcus, Escherichia und Clostridium, die sich in ihrer durchschnittlichen Abundanz zwischen männlichen und weiblichen Wachteln unterscheiden. Trotz der umfassenden Mikrobiota-Analyse ist das Zusammenspiel zwischen Tiergenetik, Ernährung, Geschlecht und Mikrobiom-Funktionalität noch nicht verstanden. Die Legehennenlinien Lohmann LSL-Classic und Lohmann Brown-Classic werden weltweit eingesetzt. Über die Wechselwirkung zwischen der Zusammensetzung des Mikrobioms, der Leistung, den Auswirkungen der Ernährung und den Veränderungen während der produktiven Lebensabschnitte, die zur Entwicklung von Fütterungsstrategien und Reaktionen des Mikrobioms in großem Maßstab beitragen könnten, ist wenig bekannt. Aufgrund der Bedeutung von P und Ca in der Geflügelernährung wurden Untersuchungen durchgeführt, um Legehennen mit reduziertem P und Ca zu füttern und die Auswirkungen auf die aktive Mikrobiota im GIT zu beschreiben. Die Linie war der Hauptfaktor für die mikrobiellen Unterschiede. Ein Kernmikrobiom aktiver Bakterien, das entlang des gesamten GIT vorhanden ist, wurde zum ersten Mal aufgedeckt und bestand aus 5 Bakterien, die in 97% aller Proben, nachgewiesen wurden (uncl. Lactobacillus, Megamonas funiformis, Ligilactobacillus salivarius, Lactobacillus helveticus, uncl. Fuscatenibacter). Außerdem wurden signifikante mikrobielle Unterschiede zwischen den GIT-Abschnitten und zwischen den Linien beschrieben. Geringfügige diätetische Auswirkungen der P- und Ca-Reduzierung auf die Mikrobiota zeigten, dass eine weitere Verringerung der Ca- und P-Supplementierung möglich sein könnte. Darüber hinaus wurde das Mikrobiom von Legehennen in fünf Produktivitätsstadien (10, 16, 24, 30 und 60 Wochen) charakterisiert, um den Alterseffekt auf das GIT-Mikrobiom zu analysieren. Obwohl die beiden Legehennenlinien das gleiche Futter erhielten und unter ähnlichen Bedingungen gehalten wurden, änderte sich die Zusammensetzung der aktiven Mikrobiota zwischen den untersuchten Produktionsstadien, der Linen und den GIT-Abschnitten. Die größte Verschiebung fand zwischen der 16. und 24. Woche statt und unterstützte die Hypothese der bakteriellen Fluktuationen aufgrund des Beginns der Legeperiode. Diese Veränderungen betrafen vor allem die Häufigkeit der Gattungen Lactobacillus und Ligilactobacillus. Es bleibt jedoch unklar, ob die Veränderungen in der Ernährung aufgrund der Entwicklung der Vögel die Verschiebungen in der Mikrobiota beeinflusst haben oder ob die anatomischen und physiologischen Veränderungen die GIT-Mikrobiota beeinflusst haben. Darüber hinaus ergab die Shotgun-Metagenomanalyse hierbei Unterschiede in den Regulationsfunktionen und -Metabolismuswegen wie auch unterschiedliche relative Häufigkeiten der mikrobiellen Zusammensetzung zwischen RNA und DNA Extraktion. Die umfassende Charakterisierung der Mikrobiota im GIT von Wachteln und zwei Hochleistungslinien von Legehennen trägt zu einem breiteren Wissen über die Dynamik des Mikrobioms im GIT von Geflügel bei. Alter und Linie spielen eine wichtigere Rolle als die Ernährung, wenn es darum geht, die Dynamik der mikrobiellen Zusammensetzung bei Legehennen und die individuelle Leistung und das Geschlecht bei Wachteln zu beeinflussen

High spatiotemporal variability of methane concentrations challenges estimates of emissions across vegetated coastal ecosystems

Coastal methane (CH4) emissions dominate the global ocean CH4 budget and can offset the "blue carbon" storage capacity of vegetated coastal ecosystems. However, current estimates lack systematic, high-resolution, and long-term data from these intrinsically heterogeneous environments, making coastal budgets sensitive to statistical assumptions and uncertainties. Using continuous CH4 concentrations, delta C-13-CH4 values, and CH4 sea-air fluxes across four seasons in three globally pervasive coastal habitats, we show that the CH4 distribution is spatially patchy over meter-scales and highly variable in time. Areas with mixed vegetation, macroalgae, and their surrounding sediments exhibited a spatiotemporal variability of surface water CH4 concentrations ranging two orders of magnitude (i.e., 6-460 nM CH4) with habitat-specific seasonal and diurnal patterns. We observed (1) delta C-13-CH signatures that revealed habitat-specific CH4 production and consumption pathways, (2) daily peak concentration events that could change >100% within hours across all habitats, and (3) a high thermal sensitivity of the CH4 distribution signified by apparent activation energies of similar to 1 eV that drove seasonal changes. Bootstrapping simulations show that scaling the CH4 distribution from few samples involves large errors, and that similar to 50 concentration samples per day are needed to resolve the scale and drivers of the natural variability and improve the certainty of flux calculations by up to 70%. Finally, we identify northern temperate coastal habitats with mixed vegetation and macroalgae as understudied but seasonally relevant atmospheric CH4 sources (i.e., releasing >= 100 mu mol CH4 m(-2) day(-1) in summer). Due to the large spatial and temporal heterogeneity of coastal environments, high-resolution measurements will improve the reliability of CH4 estimates and confine the habitat-specific contribution to regional and global CH4 budgets.Peer reviewe

Methane emissions offset atmospheric carbon dioxide uptake in coastal macroalgae, mixed vegetation and sediment ecosystems

Publisher Copyright: © 2023, The Author(s).Coastal ecosystems can efficiently remove carbon dioxide (CO2) from the atmosphere and are thus promoted for nature-based climate change mitigation. Natural methane (CH4) emissions from these ecosystems may counterbalance atmospheric CO2 uptake. Still, knowledge of mechanisms sustaining such CH4 emissions and their contribution to net radiative forcing remains scarce for globally prevalent macroalgae, mixed vegetation, and surrounding depositional sediment habitats. Here we show that these habitats emit CH4 in the range of 0.1 – 2.9 mg CH4 m−2 d−1 to the atmosphere, revealing in situ CH4 emissions from macroalgae that were sustained by divergent methanogenic archaea in anoxic microsites. Over an annual cycle, CO2-equivalent CH4 emissions offset 28 and 35% of the carbon sink capacity attributed to atmospheric CO2 uptake in the macroalgae and mixed vegetation habitats, respectively, and augment net CO2 release of unvegetated sediments by 57%. Accounting for CH4 alongside CO2 sea-air fluxes and identifying the mechanisms controlling these emissions is crucial to constrain the potential of coastal ecosystems as net atmospheric carbon sinks and develop informed climate mitigation strategies.Peer reviewe

Wide Field Spectral Imaging with Shifted Excitation Raman Difference Spectroscopy Using the Nod and Shuffle Technique

Wide field Raman imaging using the integral field spectroscopy approach was used as a fast, one shot imaging method for the simultaneous collection of all spectra composing a Raman image. For the suppression of autofluorescence and background signals such as room light, shifted excitation Raman difference spectroscopy (SERDS) was applied to remove background artifacts in Raman spectra. To reduce acquisition times in wide field SERDS imaging, we adapted the nod and shuffle technique from astrophysics and implemented it into a wide field SERDS imaging setup. In our adapted version, the nod corresponds to the change in excitation wavelength, whereas the shuffle corresponds to the shifting of charges up and down on a Charge-Coupled Device (CCD) chip synchronous to the change in excitation wavelength. We coupled this improved wide field SERDS imaging setup to diode lasers with 784.4/785.5 and 457.7/458.9 nm excitation and applied it to samples such as paracetamol and aspirin tablets, polystyrene and polymethyl methacrylate beads, as well as pork meat using multiple accumulations with acquisition times in the range of 50 to 200 ms. The results tackle two main challenges of SERDS imaging: gradual photobleaching changes the autofluorescence background, and multiple readouts of CCD detector prolong the acquisition time

Wide Field Spectral Imaging with Shifted Excitation Raman Difference Spectroscopy Using the Nod and Shuffle Technique

Wide field Raman imaging using the integral field spectroscopy approach was used as a fast, one shot imaging method for the simultaneous collection of all spectra composing a Raman image. For the suppression of autofluorescence and background signals such as room light, shifted excitation Raman difference spectroscopy (SERDS) was applied to remove background artifacts in Raman spectra. To reduce acquisition times in wide field SERDS imaging, we adapted the nod and shuffle technique from astrophysics and implemented it into a wide field SERDS imaging setup. In our adapted version, the nod corresponds to the change in excitation wavelength, whereas the shuffle corresponds to the shifting of charges up and down on a Charge-Coupled Device (CCD) chip synchronous to the change in excitation wavelength. We coupled this improved wide field SERDS imaging setup to diode lasers with 784.4/785.5 and 457.7/458.9 nm excitation and applied it to samples such as paracetamol and aspirin tablets, polystyrene and polymethyl methacrylate beads, as well as pork meat using multiple accumulations with acquisition times in the range of 50 to 200 ms. The results tackle two main challenges of SERDS imaging: gradual photobleaching changes the autofluorescence background, and multiple readouts of CCD detector prolong the acquisition time.Comment: Accepted and Published by "Sensors" Journal, 19 pages, 8 figure

A likelihood analysis of the Global Flood Monitoring ensemble product

Flooding is a natural disaster that can have devastating impacts on communities and individuals, causing significant damage to infrastructure, loss of life, and economic disruption. The Global Flood Monitoring (GFM) system of the Copernicus Emergency Management Service (CEMS) addresses these challenges and provides global, near-real time flood extent masks for each newly acquired Sentinel-1 Interferometric Wide Swath Synthetic Aperture Radar (SAR) image, as well as archive data from 2015 on, and therefore supports decision makers and disaster relief actions. The GFM flood extent is an ensemble product based on a combination of three independently developed flood mapping algorithms that individually derive the flood information from Sentinel-1 data. Each flood algorithm also provides classification uncertainty information as flood classification likelihood that is aggregated in the same ensemble process. All three algorithms utilize different methods both for flood detection and the derivation of uncertainty information. The first algorithm applies a threshold-based flood detection approach and provides uncertainty information through fuzzy memberships. The second algorithm applies a change detection approach where the classification uncertainty is expressed through classification probabilities. The third algorithm applies the Bayes decision theorem and derives uncertainty information through the posterior probability of the less probable class. The final GFM ensemble likelihood layer is computed with the mean likelihood on pixel level. As the flood detection algorithms derive uncertainty information with different methods, the value range of the three input likelihoods must be harmonized to a range from low [0] to high [100] flood likelihood. The ensemble likelihood is evaluated on two test sites in Myanmar and Somalia showcasing the performance during an actual flood event and an area with challenging conditions for SAR-based flood detection. The findings further elaborate on the statistical robustness when aggregating multiple likelihood layers. The final GFM ensemble likelihood layer serves as a simplified appraisal of trust in the ensemble flood extent detection approach. As an ensemble likelihood, it provides more robust and reliable uncertainty information for the flood detection compared to the usage of a single algorithm only. It can therefore help interpreting the satellite data and consequently to mitigate the effects of flooding and accompanied damages on communities and individuals

GFM Product User Manual

This Product User Manual (PUM) is the reference document for all end-users and stakeholders of the new Global Food Monitoring (GFM) product of the Copernicus Emergency Management Service (CEMS). The PUM provides all of the basic information to enable the proper and effective use of the GFM product and associated data output layers. This manual includes a description of the functions and capabilities of the GFM product, its applications and alternative modes of operation, and step-by-step guidance on the procedures for accessing and using the GFM product