Terminology of bioanalytical methods (IUPAC Recommendations 2018)

Recommendations are given concerning the terminology of methods of bioanalytical chemistry. With respect to dynamic development particularly in the analysis and investigation of biomacromolecules, terms related to bioanalytical samples, enzymatic methods, immunoanalytical methods, methods used in genomics and nucleic acid analysis, proteomics, metabolomics, glycomics, lipidomics, and biomolecules interaction studies are introduced

Terminology of bioanalytical methods (IUPAC Recommendations 2018)

free accessRecommendations are given concerning the terminology of methods of bioanalytical chemistry. With respect to dynamic development particularly in the analysis and investigation of biomacromolecules, terms related to bioanalytical samples, enzymatic methods, immunoanalytical methods, methods used in genomics and nucleic acid analysis, proteomics, metabolomics, glycomics, lipidomics, and biomolecules interaction studies are introduced.Peer reviewe

EUNIS Habitat Classification: Expert system, characteristic species combinations and distribution maps of European habitats

Aim: The EUNIS Habitat Classification is a widely used reference framework for European habitat types (habitats), but it lacks formal definitions of individual habitats that would enable their unequivocal identification. Our goal was to develop a tool for assigning vegetation‐plot records to the habitats of the EUNIS system, use it to classify a European vegetation‐plot database, and compile statistically‐derived characteristic species combinations and distribution maps for these habitats. Location: Europe. Methods: We developed the classification expert system EUNIS‐ESy, which contains definitions of individual EUNIS habitats based on their species composition and geographic location. Each habitat was formally defined as a formula in a computer language combining algebraic and set‐theoretic concepts with formal logical operators. We applied this expert system to classify 1,261,373 vegetation plots from the European Vegetation Archive (EVA) and other databases. Then we determined diagnostic, constant and dominant species for each habitat by calculating species‐to‐habitat fidelity and constancy (occurrence frequency) in the classified data set. Finally, we mapped the plot locations for each habitat. Results: Formal definitions were developed for 199 habitats at Level 3 of the EUNIS hierarchy, including 25 coastal, 18 wetland, 55 grassland, 43 shrubland, 46 forest and 12 man‐made habitats. The expert system classified 1,125,121 vegetation plots to these habitat groups and 73,188 to other habitats, while 63,064 plots remained unclassified or were classified to more than one habitat. Data on each habitat were summarized in factsheets containing habitat description, distribution map, corresponding syntaxa and characteristic species combination. Conclusions: EUNIS habitats were characterized for the first time in terms of their species composition and distribution, based on a classification of a European database of vegetation plots using the newly developed electronic expert system EUNIS‐ESy. The data provided and the expert system have considerable potential for future use in European nature conservation planning, monitoring and assessment

Distribution maps of vegetation alliances in Europe

Aim: The first comprehensive checklist of European phytosociological alliances, orders and classes (EuroVegChecklist) was published by Mucina et al. (2016, Applied Vegetation Science, 19 (Suppl. 1), 3–264). However, this checklist did not contain detailed information on the distribution of individual vegetation types. Here we provide the first maps of all alliances in Europe. Location: Europe, Greenland, Canary Islands, Madeira, Azores, Cyprus and the Caucasus countries. Methods: We collected data on the occurrence of phytosociological alliances in European countries and regions from literature and vegetation-plot databases. We interpreted and complemented these data using the expert knowledge of an international team of vegetation scientists and matched all the previously reported alliance names and concepts with those of the EuroVegChecklist. We then mapped the occurrence of the EuroVegChecklist alliances in 82 territorial units corresponding to countries, large islands, archipelagos and peninsulas. We subdivided the mainland parts of large or biogeographically heterogeneous countries based on the European biogeographical regions. Specialized alliances of coastal habitats were mapped only for the coastal section of each territorial unit. Results: Distribution maps were prepared for 1,105 alliances of vascular-plant dominated vegetation reported in the EuroVegChecklist. For each territorial unit, three levels of occurrence probability were plotted on the maps: (a) verified occurrence; (b) uncertain occurrence; and (c) absence. The maps of individual alliances were complemented by summary maps of the number of alliances and the alliance–area relationship. Distribution data are also provided in a spreadsheet. Conclusions: The new map series represents the first attempt to characterize the distribution of all vegetation types at the alliance level across Europe. There are still many knowledge gaps, partly due to a lack of data for some regions and partly due to uncertainties in the definition of some alliances. The maps presented here provide a basis for future research aimed at filling these gaps

Competition for soil resources forces a trade-off between enhancing tree productivity and understorey species richness in managed beech forests

Traditionally focussed on maximising productivity, forest management increasingly has to consider other functions performed by the forest stands, such as biodiversity conservation. Terrestrial plant communities typically possess a hump-back relationship between biomass productivity and plant species richness. However, there is evidence of a reverse relationship in forests dominated by beech, one of the most competitive and widespread tree species in temperate Europe. To fully explore the tree productivity-species richness relationship, we investigated above- and below-ground drivers of understorey plant species richness. We focussed on managed beech forests growing along an elevation gradient in Central Europe. We found that the lowest understorey plant diversity was under conditions optimal for beech. Tree fine root mass, canopy openness, soil C/N ratio, the interaction between tree fine root mass and stoniness, and stand structural diversity explain the variation of understorey species richness. We show that the competition for soil resources is the main driver of plant species diversity in managed forests; maximising beech growth in optimal conditions may thus come at the expense of understorey plant richness

Bioanalytical Chemistry

This chapter provides a terminology of bioanalytical chemistry in general and analysis of biomacromolecules in particular. The vocabulary given in this chapter is largely taken from Labuda et al. “Terminology of bioanalytical methods (IUPAC Recommendations 2018)”,1 which becomes the immediate source reference for definitions of terms in this chapter that are not otherwise attributed. Reference to secondary sources follow the entry as “see also:” Terms are taken from the IUPAC Recommendations published in 1994 covering mostly the analytical terminology related to body fluids, enzymology, and immunology.2 Selected terms related to bioanalysis are included within recommendations and reports devoted to the unit “katal”,3 biotechnology,4 clinical chemistry,5 toxicology,6,7 medicinal chemistry,8,9 proteomics,10 electrochemical biosensors,11,12 and physical organic chemistry.13 Definitions of some terms have been updated here with respect to new reports and considerations, and a number of new terms has been introduced particularly on the topics of “–omics”, DNA analysis and studies of the interactions between biomolecules. Terms from earlier IUPAC Recommendations that are replaced by ref. 1 are not otherwise referenced but can be found as references in ref. 1

Plot data from the paper "A modern analogue of the Pleistocene steppe-tundra ecosystem in southern Siberia" (Chytry et al.)

Primary data from 182 plots of 10 m x 10 m sampled in 12 habitat types of the steppe-tundra landscape in the SE Russian Altai Mountains in summers of 2005, 2006 and 2011. The dataset contains species composition of vascular plants, bryophytes, lichens and snails, and environmental data from the same plots including measurements of primary aboveground productivity, nutrient contents in the aboveground biomass and soil chemistry.<div><br></div><div>Sampling methods are described in the paper Chytrý et al.: A modern analogue of the Pleistocene steppe-tundra ecosystem in southern Siberia.</div><div><br></div><div>The dataset is provided in two files, one in the XLSX format for Excel 2013, another in tab-delimited table in the TXT format.</div><div><br></div><div>Plant species data were originally recorded with covers on the nine-degree Braun-Blanquet scale, which has been transformed to percentages as follows: 1, 2, 3, 4, 8, 18, 38, 63, 88. Other values can occur in case when original records of the same species in different layers were merged for the analyses. Snail data indicate species presence (1) or absence (0). NA means absence of measurement in the plot.</div><div><br></div><div><br></div

European vegetation archive (EVA). An integrated database of European vegetation plots

The European Vegetation Archive (EVA) is a centralized database of European vegetation plots developed by the IAVS Working Group European Vegetation Survey. It has been in development since 2012 and first made available for use in research projects in 2014. It stores copies of national and regional vegetation- plot databases on a single software platform. Data storage in EVA does not affect on-going independent development of the contributing databases, which remain the property of the data contributors. EVA uses a prototype of the database management software TURBOVEG 3 developed for joint management of multiple databases that use different species lists. This is facilitated by the SynBioSys Taxon Database, a system of taxon names and concepts used in the individual European databases and their corresponding names on a unified list of European flora. TURBOVEG 3 also includes procedures for handling data requests, selections and provisions according to the approved EVA Data Property and Governance Rules. By 30 June 2015, 61 databases from all European regions have joined EVA, contributing in total 1 027 376 vegetation plots, 82% of them with geographic coordinates, from 57 countries. EVA provides a unique data source for large-scale analyses of European vegetation diversity both for fundamental research and nature conservation applications. Updated information on EVA is available online at http://euroveg.org/eva-database