Search Results
Leben und Werk des Karl Hahn
Von 1905 bis zu seinem Lebensende 1946 stellte der Musiklehrer und Botaniker Karl Hahn eine umfangreiche Sammlung der Mecklenburger Flora insbesondere der Moose in der Umgebung von Neukloster und Grabow zusammen. Neben den noch vorhandenen Belegen hat er diverse Veröffentlichungen im Archiv der Freunde der Naturgeschichte in Mecklenburg hinterlassen, die neben der Beschreibung der einzelnen Funde auch Wanderbeschreibungen und Naturbeobachtungen thematisierten. In diesem Beitrag werden alle von ihm als „Neu für Mecklenburg“ bezeichneten Moosarten erstmals in einer Veröffentlichung zusammengetragen sowie Anregungen für zukünftige Arbeiten gegeben.
Natural variability or anthropogenically-induced variation? Insights from 15 years of multidisciplinary observations at the arctic marine LTER site HAUSGARTEN
Time-series studies of arctic marine ecosystems are rare. This is not surprising since polar regions are largely only accessible by means of expensive modern infrastructure and instrumentation. In 1999, the Alfred Wegener Institute, Helmholtz-Centre for Polar and Marine Research (AWI) established the LTER (Long-Term Ecological Research) observatory HAUSGARTEN crossing the Fram Strait at about 79°N. Multidisciplinary investigations covering all parts of the open-ocean ecosystem are carried out at a total of 21 permanent sampling sites in water depths ranging between 250 and 5500 m. From the outset, repeated sampling in the water column and at the deep seafloor during regular expeditions in summer months was complemented by continuous year-round sampling and sensing using autonomous instruments in anchored devices (i.e., moorings and free-falling systems). The central HAUSGARTEN station at 2500 m water depth in the eastern Fram Strait serves as an experimental area for unique biological in situ experiments at the seafloor, simulating various scenarios in changing environmental settings. Long-term ecological research at the HAUSGARTEN observatory revealed a number of interesting temporal trends in numerous biological variables from the pelagic system to the deep seafloor. Contrary to common intuition, the entire ecosystem responded exceptionally fast to environmental changes in the upper water column. Major variations were associated with a Warm-Water-Anomaly evident in surface waters in eastern parts of the Fram Strait between 2005 and 2008. However, even after 15 years of intense time-series work at HAUSGARTEN, we cannot yet predict with complete certainty whether these trends indicate lasting alterations due to anthropologically-induced global environmental changes of the system, or whether they reflect natural variability on multiyear time-scales, for example, in relation to decadal oscillatory atmospheric processes.
Semantic units: organizing knowledge graphs into semantically meaningful units of representation
Background In today’s landscape of data management, the importance of knowledge graphs and ontologies is escalating as critical mechanisms aligned with the FAIR Guiding Principles—ensuring data and metadata are Findable, Accessible, Interoperable, and Reusable. We discuss three challenges that may hinder the effective exploitation of the full potential of FAIR knowledge graphs. Results We introduce “semantic units” as a conceptual solution, although currently exemplified only in a limited prototype. Semantic units structure a knowledge graph into identifiable and semantically meaningful subgraphs by adding another layer of triples on top of the conventional data layer. Semantic units and their subgraphs are represented by their own resource that instantiates a corresponding semantic unit class. We distinguish statement and compound units as basic categories of semantic units. A statement unit is the smallest, independent proposition that is semantically meaningful for a human reader. Depending on the relation of its underlying proposition, it consists of one or more triples. Organizing a knowledge graph into statement units results in a partition of the graph, with each triple belonging to exactly one statement unit. A compound unit, on the other hand, is a semantically meaningful collection of statement and compound units that form larger subgraphs. Some semantic units organize the graph into different levels of representational granularity, others orthogonally into different types of granularity trees or different frames of reference, structuring and organizing the knowledge graph into partially overlapping, partially enclosed subgraphs, each of which can be referenced by its own resource. Conclusions Semantic units, applicable in RDF/OWL and labeled property graphs, offer support for making statements about statements and facilitate graph-alignment, subgraph-matching, knowledge graph profiling, and for management of access restrictions to sensitive data. Additionally, we argue that organizing the graph into semantic units promotes the differentiation of ontological and discursive information, and that it also supports the differentiation of multiple frames of reference within the graph.
BEXIS2: A FAIR-aligned data management system for biodiversity, ecology and environmental data
Obtaining fit-to-use data associated with diverse aspects of biodiversity, ecology and environment is challenging since often it is fragmented, sub-optimally managed and available in heterogeneous formats. Recently, with the universal acceptance of the FAIR data principles, the requirements and standards of data publications have changed substantially. Researchers are encouraged to manage the data as per the FAIR data principles and ensure that the raw data, metadata, processed data, software, codes and associated material are securely stored and the data be made available with the completion of the research.
Pelagic amphipods in the eastern fram strait with continuing presence of Themisto compressa based on sediment trap time series
Pelagic amphipods represent a large fraction of organisms entering sediment traps as so-called "swimmers." These swimmers were sampled with sediment traps (~200-300 m water depth) with two mooring arrays deployed at two different positions in the Long-Term Ecological Research observatory HAUSGARTEN in the northeastern Fram Strait. This sampling allowed us to investigate amphipod year-round abundances and inter-annual trends from 2000 onward. In this study, newly analyzed data from a 3-years period (August 2011-June 2014) are presented, extending this long-term investigation. In our results, the species Themisto abyssorum, T. libellula, and T. compressa dominated the swimmer biomass, corroborating previous studies. The observed increase of amphipod abundances persisted in all three species, additionally implying that Themisto compressa maintained its population off Svalbard, which appeared for the first time here after a warm anomaly in 2004-2007. This study provides evidence for changes in amphipod community patterns that can mainly be attributed to growing abundances of T. compressa. Similarly, another hyperiid, Lanceola clausii, also increased in abundance over the investigated period. For T. libellula, almost no juvenile individuals were recorded in the sampling period 2013/14, even though juveniles of this species were common in earlier records. The three more years of observations clearly suggest that recently documented environmental shifts persist in the eastern Fram Strait. They also highlight the merit of using sediment trap time series to obtain year-round data sets needed to reveal processes and range shift dynamics in the pelagic system on a long-term basis.
Anatomy and the type concept in biology show that ontologies must be adapted to the diagnostic needs of research
Background: In times of exponential data growth in the life sciences, machine-supported approaches are becoming increasingly important and with them the need for FAIR (Findable, Accessible, Interoperable, Reusable) and eScience-compliant data and metadata standards. Ontologies, with their queryable knowledge resources, play an essential role in providing these standards. Unfortunately, biomedical ontologies only provide ontological definitions that answer What is it? questions, but no method-dependent empirical recognition criteria that answer How does it look? questions. Consequently, biomedical ontologies contain knowledge of the underlying ontological nature of structural kinds, but often lack sufficient diagnostic knowledge to unambiguously determine the reference of a term. Results: We argue that this is because ontology terms are usually textually defined and conceived as essentialistic classes, while recognition criteria often require perception-based definitions because perception-based contents more efficiently document and communicate spatial and temporal information—a picture is worth a thousand words. Therefore, diagnostic knowledge often must be conceived as cluster classes or fuzzy sets. Using several examples from anatomy, we point out the importance of diagnostic knowledge in anatomical research and discuss the role of cluster classes and fuzzy sets as concepts of grouping needed in anatomy ontologies in addition to essentialistic classes. In this context, we evaluate the role of the biological type concept and discuss its function as a general container concept for groupings not covered by the essentialistic class concept. Conclusions: We conclude that many recognition criteria can be conceptualized as text-based cluster classes that use terms that are in turn based on perception-based fuzzy set concepts. Finally, we point out that only if biomedical ontologies model also relevant diagnostic knowledge in addition to ontological knowledge, they will fully realize their potential and contribute even more substantially to the establishment of FAIR and eScience-compliant data and metadata standards in the life sciences.
Latent Class Cluster Analysis: Selecting the number of clusters
Latent Class Cluster Analysis (LCCA) is an advanced model-based clustering method, which is increasingly used in social, psychological, and educational research. Selecting the number of clusters in LCCA is a challenging task involving inevitable subjectivity of analytical choices. Researchers often rely excessively on fit indices, as model fit is the main selection criterion in model-based clustering; it was shown, however, that a wider spectrum of criteria needs to be taken into account. In this paper, we suggest an extended analytical strategy for selecting the number of clusters in LCCA based on model fit, cluster separation, and stability of partitions. The suggested procedure is illustrated on simulated data and a real world dataset from the International Computer and Information Literacy Study (ICILS) 2018. For the latter, we provide an example of end-to-end LCCA including data preprocessing. The researcher can use our R script to conduct LCCA in a few easily reproducible steps, or implement the strategy with any other software suitable for clustering. We show that the extended strategy, in comparison to fit indices-based strategy, facilitates the selection of more stable and well-separated clusters in the data. • The suggested strategy aids researchers to select the number of clusters in LCCA • It is based on model fit, cluster separation, and stability of partitions • The strategy is useful for finding separable generalizable clusters in the data.