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Institute
Deutschland besitzt mit einer geschätzten Zahl von über 2,5 Millionen Vegetationsaufnahmen den weltweiten größten Schatz dieser wertvollen Biodiversitätsinformationen. Leider bleiben Sichtbarkeit und Zugänglichkeit dieses Erbes weit hinter den technischen Möglichkeiten und internationalen Standards zurück. Modernisierung und Ausbau der nationalen Vegetationsdatenbank vegetweb sind eine vordringliche Aufgabe, die im Rahmen eines Verbundprojektes in Angriff genommen wird. Unter http://www.vegetweb.de findet sich der Prototyp der Plattform für Vegetationsdaten, die bis Ende 2016 seine volle Funktionsfähigkeit erhalten soll.
Wir rufen alle Kolleginnen und Kollegen dazu auf, aktiv an der nationalen Vegetationsdatenbank für Deutschland mitzuarbeiten, indem sie ihre eigenen Daten zur Verfügung stellen oder sich darum bemühen, ihnen bekannte Datenbesitzer in ihrem Umfeld von einer Mitarbeit zu überzeugen.
GermanSL - Eine universelle taxonomische Referenzliste für Vegetationsdatenbanken in Deutschland
(2008)
Die GermanSL ist eine universelle elektronische Referenzliste für die Flora Deutschlands, welche von der Sektion „Taxonomische Referenzlisten“ innerhalb des Netzwerkes Phytodiversität Deutschland (NetPhyD) entwickelt und Anfang 2008 in der Version 1.0 der Öffentlichkeit zugänglich gemacht wurde. Neben der Nutzung für einzelne Vegetationsdatenbanken soll sie die leichte Verknüpfung verschiedener solcher Datenbanken untereinander und mit anderen artbezogenen Datenbanken (z. B. floristische Kartierung, plant functional traits) fördern. Die GermanSL basiert auf den publizierten Referenzlisten der botanischen Großgruppen in Deutschland, die erforderlichenfalls in genau dokumentierter Weise korrigiert oder ergänzt wurden. Sie stellt damit keine neue taxonomische Sicht dar, sondern kompiliert die vorhandenen und macht sie nutzbar. Die GermanSL enthält aktuell rund 30.000 angenommene Namen und Synonyme von Gefäßpflanzen, Moosen, Flechten und Algen, taxonomische Informationen wie die Zugehörigkeit zu übergeordneten Taxa sowie eine Auswahl an Artattributen (Zeigerwerte, Rote-Liste-Status). Die Liste steht als Excel-Datei und in einer Version für das vegetationskundliche Datenbankprogramm TURBOVEG zum kostenlosen Download bereit. Wir rufen alle interessierten Kollegen zur aktiven Mitarbeit bei der Pflege und Ergänzung der GermanSL auf.
Der Global Index of Vegetation-Plot Databases (GIVD) ist eine Metadatenbank von Vegetations - datenbanken weltweit, die im Jahr 2010 von einem internationalen Leitungsgremium ins Leben gerufen wurde und auf einem Server in Greifswald beheimatet ist. Ziel von GIVD ist es, einen besseren Überblick über die zunehmende Zahl von Vegetationsdatenbanken zu geben und ihren Inhalt für übergreifende vegetationsökologische Analysen zu erschließen. Im vorliegenden Beitrag analysieren wir, welche Daten aus Mitteleuropa (incl. Benelux-Länder) in GIVD derzeit registriert sind. Am 20. März 2011 stammten 1,35 Millionen der insgesamt registrierten 2,45 Millionen Vegetationsaufnahmen aus den 12 betrachteten Ländern. Mit über 600.000 digital verfügbaren Vegetationsaufnahmen entsprechend einer Dichte von 18 km–2 sind die Niederlande weltweit führend.
8. Workshop der Arbeitsgruppe Vegetationsdatenbanken zum Thema "Bioindikatoren" in Greifswald
(2009)
Die Arbeitsgruppe Vegetationsdatenbanken widmet sich dem Aufbau und der Auswertung von pflanzensoziologischen Datenbanken in Deutschland. Sie trifft sich seit 2002 jährlich zu bundesweiten, vom Bundesamt für Naturschutz geförderten Workshops. Seit 2008 leistet die Floristisch-soziologische Arbeitsgemeinschaft zusätzliche finanzielle Unterstützung. Das 8. Arbeitstreffen fand vom 25. bis 27. Februar 2009 auf Einladung von Michael Manthey und Florian Jansen am Institut für Botanik und Landschaftsökologie der Universität Greifswald statt und wurde von 38 TeilnehmerInnen aus 8 europäischen Ländern besucht.
Within the last 30 years the role of nitrogen in Central European forests has changed fundamentally from limiting resource to environmental problem. As the retrospective tracking of nutrient availability by soil chemical and biogeochemical measurements faces serious problems, bioindication based on understorey species composition is indispensable for monitoring broad-scale eutrophication. Based on a broad survey of more than 100,000 forest vegetation plots accessible in electronic data-bases from Germany and adjacent countries, we calculated unweighted average Ellenberg nutrient values (mN) as a proxy of plant-available macronutrients. Based on the quantiles of the frequency distribution of mN in a regionally stratified sample, we define five trophic classes, which can be used to compare dimensionless mN values. We studied spatial patterns of average nutrient values within 17 regions and compared the periods from 1899 to 1975 and 1976 to 2006. After 1975 eutrophic (mN > 5.67) and hypertrophic (mN > 6.28) conditions were common everywhere except in the Alps and Saxony-Anhalt, but very oligotrophic conditions (mN < 3.44) were still widespread in regions with nutrient-poor bedrock. Before 1975 mN of plots had been lower than after 1975 in all but the southeastern regions. Between the pre- and post-1975 data the proportion of hypertrophic plots increased from 5.7 to 11.8%, and that of very oligo-trophic plots decreased from 14.6 to 8.3%. To remove bias resulting from uneven distribution, the dataset was stratified by five tree layer dominance types, period and region and resampled. In pre-1975 plots medians of mN increased in the order Pinus sylvestris, Quercus spp., Picea abies, Fagus sylvatica and Alnus spp, whereas the increase of mN was highest in forest types with historically low nutrient values. Therefore, the widespread change in mN must be attributed to the pronounced vegetation changes in Quercus and Pinus stands, indicating the importance of land-use change, i.e. recovery of nutrient cycles after hundreds of years of exploitation through coppicing, grazing and litter use. The analysis confirms eutrophication as a megatrend of modern vegetation change and demonstrates the high research potential of linking vegetation plot databases across large regions.
Knowledge about the biogeographic affinities of the world’s tropical forests helps to better understand regional differences in forest structure, diversity, composition, and dynamics. Such understanding will enable anticipation of region-specific responses to global environmental change. Modern phylogenies, in combination with broad coverage of species inventory data, now allow for global biogeographic analyses that take species evolutionary distance into account. Here we present a classification of the world’s tropical forests based on their phylogenetic similarity. We identify five principal floristic regions and their floristic relationships: (i) Indo-Pacific, (ii) Subtropical, (iii) African, (iv) American, and (v) Dry forests. Our results do not support the traditional neo- versus paleotropical forest division but instead separate the combined American and African forests from their Indo-Pacific counterparts. We also find indications for the existence of a global dry forest region, with representatives in America, Africa, Madagascar, and India. Additionally, a northern-hemisphere Subtropical forest region was identified with representatives in Asia and America, providing support for a link between Asian and American northern-hemisphere forests.
Background: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over-as well as underestimation of true model accuracy.
Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression.
Findings Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification.
Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification – even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments – does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research.
Investigators in the cognitive neurosciences have turned to Big Data to address persistent replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. While there is tremendous potential to advance science through open data sharing, these efforts unveil a host of new questions about how to integrate data arising from distinct sources and instruments. We focus on the most frequently assessed area of cognition - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated raw data from 53 studies from around the world which measured at least one of three distinct verbal learning tasks, totaling N = 10,505 healthy and brain-injured individuals. A mega analysis was conducted using empirical bayes harmonization to isolate and remove site effects, followed by linear models which adjusted for common covariates. After corrections, a continuous item response theory (IRT) model estimated each individual subject’s latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance by 37% while preserving covariate effects. The effects of age, sex, and education on scores were found to be highly consistent across memory tests. IRT methods for equating scores across AVLTs agreed with held-out data of dually-administered tests, and these tools are made available for free online. This work demonstrates that large-scale data sharing and harmonization initiatives can offer opportunities to address reproducibility and integration challenges across the behavioral sciences.
Intrinsically disordered regions (IDRs) are essential for membrane receptor regulation but often remain unresolved in structural studies. TRPV4, a member of the TRP vanilloid channel family involved in thermo- and osmosensation, has a large N-terminal IDR of approximately 150 amino acids. With an integrated structural biology approach, we analyze the structural ensemble of the TRPV4 IDR and identify a network of regulatory elements that modulate channel activity in a hierarchical lipid-dependent manner through transient long-range interactions. A highly conserved autoinhibitory patch acts as a master regulator by competing with PIP2 binding to attenuate channel activity. Molecular dynamics simulations show that loss of the interaction between the PIP2-binding site and the membrane reduces the force exerted by the IDR on the structured core of TRPV4. This work demonstrates that IDR structural dynamics are coupled to TRPV4 activity and highlights the importance of IDRs for TRP channel function and regulation.