Der Einfluss der Fruchtfolge auf die Beikrautflora im ökologischen Landbau

Im ökologischen Landbau ist die Fruchtfolge ein wichtiges Element in der Unkrautregulierung. Fünf verschiedene Fruchtfolgevarianten wurden in einem Parzellenversuch in Oberbayern auf ihre Wirkung auf den Beikrautbesatz untersucht. Dafür wurde in den Jahren 2013-2016 die Beikrautvegetation (alle Arten und ihre Deckung) auf den Versuchsparzellen erfasst. Insgesamt wurden in den 120 Aufnahmen 93 Pflanzenarten registriert; im Mittel 26 Arten pro 40 m². Für die Kartoffeln ergab sich lediglich ein Unterschied in der Kulturdeckung, die in der Fruchtfolge mit Gülledüngung und zweijährigem Kleegras höher war als in den anderen beiden Kartoffel-Fruchtfolgen. Die Beikrautvegetation unterschied sich kaum zwischen den Fruchtfolgen. In der Sommer-Gerste konnte durch eine einjährige Kleegrasphase im Vergleich zur Ackerbohnenkultur die Beikrautdeckung geringer gehalten werden, u. a. durch einen geringeren Besatz mit Wurzelunkräutern wie Sonchus arvensis oder eine niedrigere Deckung von Avena fatua. Ähnliches zeigte sich auch im Winterweizen. Die Fruchtfolgevariante ohne Kartoffeln und Kleegras bot sowohl Wurzel- als auch Samenunkräutern eine bessere Etablierungschance, so dass auch die gesamte Wildkrautdeckung hier höher war

Artenanreicherung im Wirtschaftsgrünland – Projekt Transfer

Grünland kann sehr artenreich sein und nimmt eine Schlüsselrolle bei der Erhaltung der Artenvielfalt in der Kulturlandschaft ein. Artenreiche Flächen sind inzwischen aber selten geworden und auch wenn eine intensive Nutzung wieder aufgegeben wird, kommen die Wiesenarten oft nicht zurück. So entsteht artenarmes, wenig intensiv genutztes Grünland mit geringem Ertrag. Ziel des Projektes „Transfer“ ist die Artenanreicherung im Wirtschaftsgrünland mittels Mahdgutübertragung bzw. Ansaat von gebietseigenem Saatgut. Besonders wichtig ist dabei die Erprobung der praktischen Durchführung durch Landwirte, für die ein Leitfaden zur Artenanreicherung entwickelt wird. Besonders im Öko-Landbau besteht ein hohes Interesse, die Biodiversität im Betrieb zu erhöhen. Die Artenzahl konnte auf allen Projektflächen erhöht werden. Im ersten Jahr nach der Mahdgutübertragung konnten zwischen 9 und 22 von der Spenderfläche übertragene Arten auf der Empfängerfläche nachgewiesen werden, die nicht im Ausgangsbestand vorhanden waren. Auf den Ansaatflächen konnten fast alle ausgebrachten Arten etabliert werden

ReSurveyGermany: Vegetation-plot time-series over the past hundred years in Germany

Vegetation-plot resurvey data are a main source of information on terrestrial biodiversity change, with records reaching back more than one century. Although more and more data from re-sampled plots have been published, there is not yet a comprehensive open-access dataset available for analysis. Here, we compiled and harmonised vegetation-plot resurvey data from Germany covering almost 100 years. We show the distribution of the plot data in space, time and across habitat types of the European Nature Information System (EUNIS). In addition, we include metadata on geographic location, plot size and vegetation structure. The data allow temporal biodiversity change to be assessed at the community scale, reaching back further into the past than most comparable data yet available. They also enable tracking changes in the incidence and distribution of individual species across Germany. In summary, the data come at a level of detail that holds promise for broadening our understanding of the mechanisms and drivers behind plant diversity change over the last century

<scp>ReSurveyEurope</scp>: A database of resurveyed vegetation plots in Europe

AbstractAimsWe introduce ReSurveyEurope — a new data source of resurveyed vegetation plots in Europe, compiled by a collaborative network of vegetation scientists. We describe the scope of this initiative, provide an overview of currently available data, governance, data contribution rules, and accessibility. In addition, we outline further steps, including potential research questions.ResultsReSurveyEurope includes resurveyed vegetation plots from all habitats. Version 1.0 of ReSurveyEurope contains 283,135 observations (i.e., individual surveys of each plot) from 79,190 plots sampled in 449 independent resurvey projects. Of these, 62,139 (78%) are permanent plots, that is, marked in situ, or located with GPS, which allow for high spatial accuracy in resurvey. The remaining 17,051 (22%) plots are from studies in which plots from the initial survey could not be exactly relocated. Four data sets, which together account for 28,470 (36%) plots, provide only presence/absence information on plant species, while the remaining 50,720 (64%) plots contain abundance information (e.g., percentage cover or cover–abundance classes such as variants of the Braun‐Blanquet scale). The oldest plots were sampled in 1911 in the Swiss Alps, while most plots were sampled between 1950 and 2020.ConclusionsReSurveyEurope is a new resource to address a wide range of research questions on fine‐scale changes in European vegetation. The initiative is devoted to an inclusive and transparent governance and data usage approach, based on slightly adapted rules of the well‐established European Vegetation Archive (EVA). ReSurveyEurope data are ready for use, and proposals for analyses of the data set can be submitted at any time to the coordinators. Still, further data contributions are highly welcome.</jats:sec

Summer rain and wet soil rather than management affect the distribution of a toxic plant in production grasslands

Abstract In the northern forelands of the Alps, farmers report an increase of Jacobaea aquatica in production grasslands. Due to its toxicity, the species affects grassland productivity and calls for costly control measures. We are investigating the extent to which management practices or climatic factors are responsible for the increase of the species and how the situation will change due to climate change. We tested for effects of management intensity, fertilization, agri-environmental measures, and soil disturbance, and modeled the occurrence of the species under rcp4.5 and rcp8.5 scenarios. The main determinants of the occurrence of the species are soil type and summer rainfall. A high risk is associated with wet soils and > 400 mm of rain between June and August; an influence of the management-related factors could not be detected. Under the climate-change scenarios, the overall distribution decreases and shifts to the wetter alpine regions. Thus, the current increase is rather a shift in the occurrence of the species due to the altered precipitation situation. Under future climatic conditions, the species will decline and retreat to higher regions in the Alps. This will decrease the risk of forage contamination for production grassland in the lowlands

Deep interactome learning for de novo drug design

De novo drug design aims to generate molecules from scratch that possess specific chemical and pharmacological properties. We present a computational approach utilizing interactome-based deep learning for ligand- and structure-based generation of drug-like molecules. This method capitalizes on the unique strengths of both graph neural networks and chemical language models, offering an alternative to the need for application-specific reinforcement, transfer, or few-shot learning. It allows for the construction of compound libraries tailored to possess specific bioactivity, synthesizability, and structural novelty. In order to proactively evaluate the deep interactome learning framework for structure-based drug design, potential new ligands targeting the binding site of the human peroxisome proliferator-activated receptor (PPAR) subtype gamma were generated. The top-ranking designs were chemically synthesized and biophysically and biochemically characterized. Potent PPAR partial agonists were identified, demonstrating favorable activity and the desired selectivity profiles for both nuclear receptors and off-target interactions. Crystal structure determination of the ligand-receptor complex confirmed the anticipated binding mode. This successful outcome positively advocates interactome-based de novo design for application in bioorganic and medicinal chemistry, enabling the creation of innovative bioactive molecules

Prospective de novo drug design with deep interactome learning

Abstract De novo drug design aims to generate molecules from scratch that possess specific chemical and pharmacological properties. We present a computational approach utilizing interactome-based deep learning for ligand- and structure-based generation of drug-like molecules. This method capitalizes on the unique strengths of both graph neural networks and chemical language models, offering an alternative to the need for application-specific reinforcement, transfer, or few-shot learning. It enables the “zero-shot" construction of compound libraries tailored to possess specific bioactivity, synthesizability, and structural novelty. In order to proactively evaluate the deep interactome learning framework for protein structure-based drug design, potential new ligands targeting the binding site of the human peroxisome proliferator-activated receptor (PPAR) subtype gamma are generated. The top-ranking designs are chemically synthesized and computationally, biophysically, and biochemically characterized. Potent PPAR partial agonists are identified, demonstrating favorable activity and the desired selectivity profiles for both nuclear receptors and off-target interactions. Crystal structure determination of the ligand-receptor complex confirms the anticipated binding mode. This successful outcome positively advocates interactome-based de novo design for application in bioorganic and medicinal chemistry, enabling the creation of innovative bioactive molecules

Prospective deep interactome learning for de novo drug design

Source data of the publication. The compressed .zip file contains the 37 .csv files and 1 .pdb file