Palaeoassocia as methodological tool for phytosociological analyses is further developed

Detailed understanding of the environment surrounding an archaeological site has always been a major goal in archaeobotanical analyses. Various methods have been used to characterize the environment, reaching from indicator species to judge upon specific environmental properties (e.g. salinity, acidity) to indicator values for all species in a spectrum.From an ecological perspective however, a disadvantage of this individualistic approach is that the interplay between plant species is largely ignored. The spatial manifestation and co-occurrence of plant species is known as vegetation. A basic understanding of vegetation is often acquired by grouping species in ‘ecological groups’ based on individual labelling of species as, for example, ‘arable weed’, ‘grassland species’, or ‘ruderal’.Another approach to plant sociology is phytosociology, the study of plant communities (syntaxa). Plant communities, as opposed to ecological groups, are defined on the basis of field observations of co-occurrence of species. Although substantial variation in field methods and research density exists, numerous systematic vegetation recordings (relevés or plots) are available in most countries.In the Netherlands, this adds up to more than 600000. In a previous paper we presented a method to systematically and objectively analyse archaeobotanical datasets (Schepers et al. 2013). We did so by developing an additional function for the associa software package (Van Tongeren et al. 2008). This package assigns plots from field observations to pre-defined and well-established plant communities. This version, palaeoassocia, basically treats archaeobotanical datasets as modern plots, but evidently requires some modifications. A major challenge to overcome is that the vast majority of archaeobotanical assemblages contain plant species from various environmental origins. The 2013 version is very time-consuming, requires substantial practice and experience, and was therefore potentially subject to interpersonal differences. Thus, we felt the need to develop a more automated, user-friendly, and faster version. A rich dataset from the Southern Netherlands village of Best, provided an excellent test set for this further development.<br/

PALAEOASSOCIA as a methodological tool for phytosociological analyses is further developed

The earlier version of PALAEOASSOCIA involved a considerable input of manual labour in sorting species tables with association data to identify plant communities that could have been present. A large archaeobotanical dataset from the site of Best (The Netherlands) was used to judge whether this manual sorting results in subjective results. As these were found, we developed a fully automatic version of PALAEOASSOCIA, including this sorting process. Likelihood clustering with prior probability yielded the highest number of associations recovered from four samples, and was therefore chosen as the optimal clustering method. The sorted tables are automatically converted to syntaxonomical groups. The hierarchical level of these groups can be pre-defined by the user of the program. Syntaxa that are highly improbable geographically cannot be ruled out a priori, but need to be removed manually. PALAEOASSOCIA is not meant to replace other methods of ecological interpretations of archaeobotanical data, but instead as a tool to obtain a more detailed result

Effects of Oxygen Limitation on the Biosynthesis of Photo Pigments in the Red Microalgae Galdieria sulphuraria Strain 074G

As a consequence of the inhibition of one of the steps in the biosynthesis of the photopigments chlorophyll and phycobilin, the red microalga Galdieria partita excretes coproporphyrinogen III in the medium when growing on glucose. No coproporphyrinogen III was found when the closely related red microalgae G. sulphuraria strain 074G was grown on glucose and excessive amounts of oxygen. When under the same conditions oxygen was limiting, coproporphyrinogen III was present in the medium. We conclude that not glucose but the amount of oxygen in the medium results in the accumulation of coproporphyrinogen III. This is explained by the inactivition of the oxygen-dependent coproporphyrinogen III oxidase that converts coproporhyrinogen III to protoporphyrinogen IX, one of the intermediate steps in the biosynthesis of chlorophyl and phycobilin

Ensemble-based satellite-derived carbon dioxide and methane column-averaged dry-air mole fraction data sets (2003-2018) for carbon and climate applications

Satellite retrievals of column-averaged dry-air mole fractions of carbon dioxide (CO₂) and methane (CH₄), denoted XCO₂ and XCH₄, respectively, have been used in recent years to obtain information on natural and anthropogenic sources and sinks and for other applications such as comparisons with climate models. Here we present new data sets based on merging several individual satellite data products in order to generate consistent long-term climate data records (CDRs) of these two Essential Climate Variables (ECVs). These ECV CDRs, which cover the time period 2003–2018, have been generated using an ensemble of data products from the satellite sensors SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT and (for XCO₂) for the first time also including data from the Orbiting Carbon Observatory 2 (OCO-2) satellite. Two types of products have been generated: (i) Level 2 (L2) products generated with the latest version of the ensemble median algorithm (EMMA) and (ii) Level 3 (L3) products obtained by gridding the corresponding L2 EMMA products to obtain a monthly 5∘×5∘ data product in Obs4MIPs (Observations for Model Intercomparisons Project) format. The L2 products consist of daily NetCDF (Network Common Data Form) files, which contain in addition to the main parameters, i.e., XCO₂ or XCH₄, corresponding uncertainty estimates for random and potential systematic uncertainties and the averaging kernel for each single (quality-filtered) satellite observation. We describe the algorithms used to generate these data products and present quality assessment results based on comparisons with Total Carbon Column Observing Network (TCCON) ground-based retrievals. We found that the XCO₂ Level 2 data set at the TCCON validation sites can be characterized by the following figures of merit (the corresponding values for the Level 3 product are listed in brackets) – single-observation random error (1σ): 1.29 ppm (monthly: 1.18 ppm); global bias: 0.20 ppm (0.18 ppm); and spatiotemporal bias or relative accuracy (1σ): 0.66 ppm (0.70 ppm). The corresponding values for the XCH₄ products are single-observation random error (1σ): 17.4 ppb (monthly: 8.7 ppb); global bias: −2.0 ppb (−2.9 ppb); and spatiotemporal bias (1σ): 5.0 ppb (4.9 ppb). It has also been found that the data products exhibit very good long-term stability as no significant long-term bias trend has been identified. The new data sets have also been used to derive annual XCO₂ and XCH₄ growth rates, which are in reasonable to good agreement with growth rates from the National Oceanic and Atmospheric Administration (NOAA) based on marine surface observations. The presented ECV data sets are available (from early 2020 onwards) via the Climate Data Store (CDS, https://cds.climate.copernicus.eu/, last access: 10 January 2020) of the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/, last access: 10 January 2020)

End report ID3AS AirOPT

Large wind turbines have recently had the energy sector’s attention given their reliability, cost-effectiveness, competitive power, and the maturity of their technology. Unfortunately, large wind turbines usually face obstacles regarding their social acceptance near rural or residential areas. On the other hand, small wind turbines are better accepted in these areas as their presence is perceived as less threatening. For this, Tandem Wind Energy (TWE) has developed a small wind turbine concept that would be robust, reliable, and affordable. The TWE turbine shall especially target rural areas in developing countries where the access to the grid is limited. To ensure that the turbine will deliver as promised, the turbine has been heavily instrumented with different sensors on the rotor and the tower to measure its performance. Prior measuring its performance, a simulation model was developed by the Emden/Leer consortium team members to estimate the aerodynamic performance of the wind turbine. The output of this model would be validated against the measurements from the test site. The wind turbine was then assembled and erected successfully at the Hanzehogeschool test site. The wind turbine concept has proven to be successful yet due to complications, the test field measurements could not be obtained in time, which shall result in the continuation of the analysis beyond the ID3AS project in 2021