Three Phenolic and a Sterol Glycosides Identified for the First Time in Matthiola longipetala Growing in Tunisia

Three phenolic glycosides: 4-O-ß-D-glycopyranosyl zingerone 1, 4-O-ß-D-glycopyranosylhomovanillyl alcohol 2 and eugenol glycoside 3, together with 3-O-ß-D-glycopyranosyl sitosterol 4, were isolated and identified for the first time from the flowers of Matthiola longipetala (Crucifers) growing in Tunisia. The structures of 1, 2 and 3 were identified via their acetylated derivatives on the basis of the 1 and 2D NMR data analysis, mass spectrometry and IR spectroscopy

Fertilizer Potential of Organic-Based Soil Amendments on cv. Sangiovese (V. vinifera L.) Vines: Preliminary Results

The intensification of highly specialized viticulture has led to a dramatic decrease of soil fertility that can be restored by increasing soil organic matter using organic fertilizers. The aim of the present experiment was to evaluate the effect of different organic amendments on vine vegetative growth and nutritional status, soil N availability and microbial biomass, as well as on yield and grape quality. The experiment was carried out in 2020 and 2021, on cv. Sangiovese (Vitis vinifera L.) vines grafted on 110 Richter (V. berlandieri × V. rupestris) planted in February 2019. Plants were fer-tilized yearly in spring with (1) mineral fertilization (MIN), (2) municipal organic waste compost (MOW), and (3) sewage sludge compost (SS). The application of SS increased nitrate availability in both years, while the supply of organic matter (no matter the source) enhanced soil microbial bio-mass content. Plant nutritional status was in the optimal range for all treatments, with an increase of N in SS and K in MOW. Fruit yield in 2020 was not influenced by treatments, while in 2021 it was enhanced by MIN and MOW, which also induced a higher berry quality. Plant vegetative growth was stimulated by the application of SS. In conclusion, from these preliminary results we observed a higher N availability as a consequence of SS supply that resulted in a higher plant biomass, but reduced yield and berry quality, supporting the theory that for vineyards, N should be carefully managed to reach an equilibrium between vegetative and reproductive activity

Effectiveness of synthetic calcite doped with Fe-EDDHSA as a slow-release Fe source: In-vitro experiment on kiwifruit (Actinidia chinensis var. deliciosa) plants

Doped calcite (Fe-EDDHSA/CaCO3) was experimentally produced. The hypothesis of the present experiment is that, when roots get in contact with Fe-EDDHSA/CaCO3, the extrusion of H+ decreases the pH and dissolves calcite with subsequent release of Fe that becomes available for roots. The aim of the experiment was to determine whether doped calcite might represent a slow-release Fe source for in-vitro grown kiwifruit plantlets. The root elongation media used in the experiment had pH 8.0 and differed from each other for Fe supply as follow: Control medium that contained complete Murashige and Skoog salt mixture, including FeSO4 and Na(2)EDTA; calcite medium enriched with Fe-EDDHSA/CaCO3 as the only Fe source; -Fe medium without Fe. The absence of FeSO4 in the medium caused a reduction of plantlet growth. The final pH was higher with calcite medium than in control and -Fe. The addition of Fe-EDDHSA/CaCO3 increased Fe shoot concentration when compared with the -Fe medium. The data of the present experiment show the potential Fe slow release ability of Fe-EDDHSA/CaCO3; however, further investigation on Fe containing fertilizers should be conducted on potted plants to validate our result

Uncertainty in ocean-color estimates of chlorophyll for phytoplankton groups

This is the final version. Available from Frontiers Media via the DOI in this record.Over the past decade, techniques have been presented to derive the community structure of phytoplankton at synoptic scales using satellite ocean-color data. There is a growing demand from the ecosystem modeling community to use these products for model evaluation and data assimilation. Yet, from the perspective of an ecosystem modeler these products are of limited use unless: (i) the phytoplankton products provided by the remote-sensing community match those required by the ecosystem modelers; and (ii) information on per-pixel uncertainty is provided to evaluate data quality. Using a large dataset collected in the North Atlantic, we re-tune a method to estimate the chlorophyll concentration of three phytoplankton groups, partitioned according to size [pico- (20 μm)]. The method is modified to account for the influence of sea surface temperature, also available from satellite data, on model parameters and on the partitioning of microphytoplankton into diatoms and dinoflagellates, such that the phytoplankton groups provided match those simulated in a state of the art marine ecosystem model (the European Regional Seas Ecosystem Model, ERSEM). The method is validated using another dataset, independent of the data used to parameterize the method, of more than 800 satellite and in situ match-ups. Using fuzzy-logic techniques for deriving per-pixel uncertainty, developed within the ESA Ocean Colour Climate Change Initiative (OC-CCI), the match-up dataset is used to derive the root mean square error and the bias between in situ and satellite estimates of the chlorophyll for each phytoplankton group, for 14 different optical water types (OWT). These values are then used with satellite estimates of OWTs to map uncertainty in chlorophyll on a per pixel basis for each phytoplankton group. It is envisaged these satellite products will be useful for those working on the validation of, and assimilation of data into, marine ecosystem models that simulate different phytoplankton groups.National Centre for Earth Observation (NCEO)European Space Agency (ESA)NERC-UK ECOMA

Advancing Marine Biogeochemical and Ecosystem Reanalyses and Forecasts as Tools for Monitoring and Managing Ecosystem Health

Ocean ecosystems are subject to a multitude of stressors, including changes in ocean physics and biogeochemistry, and direct anthropogenic influences. Implementation of protective and adaptive measures for ocean ecosystems requires a combination of ocean observations with analysis and prediction tools. These can guide assessments of the current state of ocean ecosystems, elucidate ongoing trends and shifts, and anticipate impacts of climate change and management policies. Analysis and prediction tools are defined here as ocean circulation models that are coupled to biogeochemical or ecological models. The range of potential applications for these systems is broad, ranging from reanalyses for the assessment of past and current states, and short-term and seasonal forecasts, to scenario simulations including climate change projections. The objectives of this article are to illustrate current capabilities with regard to the three types of applications, and to discuss the challenges and opportunities. Representative examples of global and regional systems are described with particular emphasis on those in operational or pre-operational use. With regard to the benefits and challenges, similar considerations apply to biogeochemical and ecological prediction systems as do to physical systems. However, at present there are at least two major differences: (1) biogeochemical observation streams are much sparser than physical streams presenting a significant hinderance, and (2) biogeochemical and ecological models are largely unconstrained because of insufficient observations. Expansion of biogeochemical and ecological observation systems will allow for significant advances in the development and application of analysis and prediction tools for ocean biogeochemistry and ecosystems, with multiple societal benefits

Uncertainty in ocean-colour estimates of chlorophyll for phytoplankton groups

Over the past decade, techniques have been presented to derive the community structure of phytoplankton at synoptic scales using satellite ocean-color data. There is a growing demand from the ecosystem modeling community to use these products for model evaluation and data assimilation. Yet, from the perspective of an ecosystem modeler these products are of limited use unless: (i) the phytoplankton products provided by the remote-sensing community match those required by the ecosystem modelers; and (ii) information on per-pixel uncertainty is provided to evaluate data quality. Using a large dataset collected in the North Atlantic, we re-tune a method to estimate the chlorophyll concentration of three phytoplankton groups, partitioned according to size [pico- (20 μm)]. The method is modified to account for the influence of sea surface temperature, also available from satellite data, on model parameters and on the partitioning of microphytoplankton into diatoms and dinoflagellates, such that the phytoplankton groups provided match those simulated in a state of the art marine ecosystem model (the European Regional Seas Ecosystem Model, ERSEM). The method is validated using another dataset, independent of the data used to parameterize the method, of more than 800 satellite and in situ match-ups. Using fuzzy-logic techniques for deriving per-pixel uncertainty, developed within the ESA Ocean Colour Climate Change Initiative (OC-CCI), the match-up dataset is used to derive the root mean square error and the bias between in situ and satellite estimates of the chlorophyll for each phytoplankton group, for 14 different optical water types (OWT). These values are then used with satellite estimates of OWTs to map uncertainty in chlorophyll on a per pixel basis for each phytoplankton group. It is envisaged these satellite products will be useful for those working on the validation of, and assimilation of data into, marine ecosystem models that simulate different phytoplankton groups

Biogeochemical Model Optimization by Using Satellite-Derived Phytoplankton Functional Type Data and BGC-Argo Observations in the Northern South China Sea

Marine biogeochemical models have been widely used to understand ecosystem dynamics and biogeochemical cycles. To resolve more processes, models typically increase in complexity, and require optimization of more parameters. Data assimilation is an essential tool for parameter optimization, which can reduce model uncertainty and improve model predictability. At present, model parameters are often adjusted using sporadic in-situ measurements or satellite-derived total chlorophyll-a concentration at sea surface. However, new ocean datasets and satellite products have become available, providing a unique opportunity to further constrain ecosystem models. Biogeochemical-Argo (BGC-Argo) floats are able to observe the ocean interior continuously and satellite phytoplankton functional type (PFT) data has the potential to optimize biogeochemical models with multiple phytoplankton species. In this study, we assess the value of assimilating BGC-Argo measurements and satellite-derived PFT data in a biogeochemical model in the northern South China Sea (SCS) by using a genetic algorithm. The assimilation of the satellite-derived PFT data was found to improve not only the modeled total chlorophyll-a concentration, but also the individual phytoplankton groups at surface. The improvement of simulated surface diatom provided a better representation of subsurface particulate organic carbon (POC). However, using satellite data alone did not improve vertical distributions of chlorophyll-a and POC. Instead, these distributions were improved by combining the satellite data with BGC-Argo data. As the dominant variability of phytoplankton in the northern SCS is at the seasonal timescale, we find that utilizing monthly-averaged BGC-Argo profiles provides an optimal fit between model outputs and measurements in the region, better than using high-frequency measurements

Assimilation of remotely-sensed optical properties to improve marine biogeochemistry modelling

In this paper we evaluate whether the assimilation of remotely-sensed optical data into a marine ecosystem model improves the simulation of biogeochemistry in a shelf sea. A localized Ensemble Kalman filter was used to assimilate weekly diffuse light attenuation coefficient data, Kd(443) from SeaWiFs, into an ecosystem model of the western English Channel. The spatial distributions of (unassimilated) surface chlorophyll from satellite, and a multivariate time series of eighteen biogeochemical and optical variables measured in situ at one long-term monitoring site were used to evaluate the system performance for the year 2006. Assimilation reduced the root mean square error and improved the correlation with the assimilated Kd(443) observations, for both the analysis and, to a lesser extent, the forecast estimates, when compared to the reference model simulation. Improvements in the simulation of (unassimilated) ocean colour chlorophyll were less evident, and in some parts of the Channel the simulation of this data deteriorated. The estimation errors for the (unassimilated) in situ data were reduced for most variables with some exceptions, e.g. dissolved nitrogen. Importantly, the assimilation adjusted the balance of ecosystem processes by shifting the simulated food web towards the microbial loop, thus improving the estimation of some properties, e.g. total particulate carbon. Assimilation of Kd(443) outperformed a comparative chlorophyll assimilation experiment, in both the estimation of ocean colour data and in the simulation of independent in situ data. These results are related to relatively low error in Kd(443) data, and because it is a bulk optical property of marine ecosystems. Assimilation of remotely-sensed optical properties is a promising approach to improve the simulation of biogeochemical and optical variables that are relevant for ecosystem functioning and climate change studies

Ocean Biology Studied from Space

This is the final version. Available on open access from Springer via the DOI in this recordVisible spectral radiometric measurements from space, commonly referred to as ocean-colour measurements, provide a rich stream of information on ocean biota as well as on biological and ecosystem processes. The strength of the ocean-colour technology for observing marine life lies in its global reach, combined with its ability to sample the field at a variety of spatial and temporal scales that match the scales of the processes themselves. Another advantage lies in the growing length of the time series of ocean-colour-derived products, enabiling investigations into any long-term changes, if present. This paper presents an overview of the principles and applications of ocean-colour data. The concentration of chlorophyll-a, the major pigment present in phytoplankton–single-celled, free-floating plants that are present in the sunlit layers of the ocean–was the first, and remains the most common, biological variable derived from ocean-colour data. Over the years, the list of ocean-colour products have grown to encompass many measures of the marine ecosystem and its functions, including primary production, phenology and ecosystem structure. Applications that exploit the data are many and varied, and include ecosystem-based fisheries management, biogeochemical cycles in the ocean, ecosystem health and climate change. An integrated approach, incorporating other modes of ocean observations and models with satellite observations, is needed to investigate the mysteries of the marine ecosystem