The Petrophytic Steppes of the Urals: Diversity and Ecological Drivers

The diversity and main compositional patterns of the petrophytic steppes of the Urals were studied. Two questions were considered in detail: (i) How rich is the phytocoenotic diversity of the petrophytic steppes? and (ii) What kind of ecological drivers determine its differentiation? A dataset of 1,025 relevés was compiled,representing communities of different climatic and geological conditions. Using formalized classification (TWINSPAN algorithm), eight vegetation types on the petrophytic steppes were defined. DCA-ordination was used to determine the main ecological drivers (both climatic and edaphic) of plant communities’ diversity. Among them are mean annual temperature and precipitation, aridity, rockiness and local habitat moisture. The interaction of different ecological and geographical factors leads to high levels of floristic and coenotic diversity of vegetation in dry rocky habitats. Keywords: petrophytic steppes, ecological drivers, ordination, Southern Ural

A Database of Weed Plants in the European Part of Russia

Weeds are plants that, although not specially cultivated, grow and often adapt to growing in arable lands. They form an ecological variant of flora, as a historically-formed set of species growing on cultivated soils. For the rational use of the chemical and biological crop protection products and to produce safe and high-quality food, up-to-date data on the floristic diversity of weeds and the patterns of its geographical change are required. The need for a weeds' database arises that allows many specialists to work together independently. However, the great value of any database lies not in its existence, but in the accumulation of data that can be used to analyse the factors affecting the species diversity of weeds. New information A dataset of weed species diversity and their distribution in the European part of Russia, based on the results of the authors' own research from 1999 to 2019, has been created. The dataset includes 24,284 observations of occurrences of weed plants, which were obtained on the basis of 2,049 relevés of segetal plant communities in seven regions of the European part of Russia. In total, the dataset includes information about 329 species of vascular plants growing in 65 farmlands: cereals, spring and winter crops, industrial crops, row crops and perennial grasses (Tretyakova et al. 2020). © Tretyakova A et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Funding: This work was supported by Russian public funds (AAAA-A18-118011990151-7) in the framework of implementation of the State task on the “Vascular plants of Eurasia: taxonomy, ora, plant resources” (AAAA-A19-119031290052-1), by the Competitiveness of the Ural Federal University (Russian Federation Government Regulation no. 211, contract no. 02. A03.21.0006) and partially by the Russian Foundation for Basic Research (project nos. 17-44-020402 and 19-016-00135)

Сегетальная флора некоторых регионов России: характеристика таксономической структуры

The authors' data on the weed species composition in 8 geographic regions of the Russian Federation were laid at the basis of this survey. The segetal flora comprised weeds of cereals, root crops, and perennial grasses. The composition and taxonomic structure of weed species were analyzed. The total number of weedy plants encompassed 686 plant species. The level of regional weed species diversity was positively related to the area planted. Altai Territory, Udmurtia, and Rostov Province had the greatest weed species diversity, with more than 300 species, while Vologda Province had the lowest diversity (193 species). Most species-rich plant families (Asteraceae Dumort., Poaceae Barnhart, Fabaceae Lindl., Brassicaceae Burnet), genera (Potentila L., Artemisia L., Veronica L., Chenopodium L., Silene L., Trifolium L.), their subsequences, contributions of single-species families (17-39%) and genera (57-74%) were relatively stable systematic structure indicators. Only 18% of the species were common for all the regions. In each region there were region-specific groups of species. Weed species compositions in geographically close regions - the European part of Russia and the Urals - showed the greatest similarity. As for geographically distant regions (Altai Territory and Rostov Province), their weedy species compositions were distant and contained large groups of region-specific species. © 2020 All-Russian Institute of Plant Genetic Resources -Federal Research Center. All rights reserved.This work was supported in part by the Russian Foundation for Basic Research (projects 17-44-020402 р_а, 19-016-00135), state budget funds (AAAA-A18-118011990151-7), and as part of the implementation of the state task on the topic: “Vascular plants of Eurasia: taxonomy, flora, plant resources” (AAAA-A19-119031290052-1)

Interregional features in the taxonomic composition of the Russian segetal floras

The article contains a comparative analysis of the taxonomical structure of the weedy species composition (segetal flora) in eight regions of Russian Federation: Leningrad, Novgorod, Vologda, Rostov and Sverdlovsk Provinces, Udmurt Republic, Republic of Bashkortostan, and Altai Territory. The segetal flora comprised weeds of cereals, root crops and perennial grasses. The comparison was made separately for the native and alien weeds. The number of native species was higher than that of alien species and varied from 137 to 209 species. The number of alien weeds varied from 99 to 179 species. Vologda Province had the lowest diversity of both native and alien plant species. Udmurt Republic had the greatest native species diversity and Altai Territory had the greatest alien species diversity. The Asteraceae, Poaceae, Brassicaceae, Fabaceae, Lamiaceae, Caryophyllaceae and Scrophulariaceae families dominated in both the native and alien fractions. The authors compared the compositions of species, families and genera of native and alien weeds. Native and alien weedy species showed the greatest similarity in their composition in geographically close regions: European Russia and the Urals. As for geographically remote regions – Altai Territory and Rostov Province – native and alien weedy species compositions were distant. At the same time, the levels of similarity among the native species were lower than among the alien ones. This attests to greater variability in the species composition among native weeds than among alien ones

sPlotOpen – An environmentally balanced, open-access, global dataset of vegetation plots

Assessing biodiversity status and trends in plant communities is critical for understanding, quantifying and predicting the effects of global change on ecosystems. Vegetation plots record the occurrence or abundance of all plant species co-occurring within delimited local areas. This allows species absences to be inferred, information seldom provided by existing global plant datasets. Although many vegetation plots have been recorded, most are not available to the global research community. A recent initiative, called ?sPlot?, compiled the first global vegetation plot database, and continues to grow and curate it. The sPlot database, however, is extremely unbalanced spatially and environmentally, and is not open-access. Here, we address both these issues by (a) resampling the vegetation plots using several environmental variables as sampling strata and (b) securing permission from data holders of 105 local-to-regional datasets to openly release data. We thus present sPlotOpen, the largest open-access dataset of vegetation plots ever released. sPlotOpen can be used to explore global diversity at the plant community level, as ground truth data in remote sensing applications, or as a baseline for biodiversity monitoring. Main types of variable contained: Vegetation plots (n = 95,104) recording cover or abundance of naturally co-occurring vascular plant species within delimited areas. sPlotOpen contains three partially overlapping resampled datasets (c. 50,000 plots each), to be used as replicates in global analyses. Besides geographical location, date, plot size, biome, elevation, slope, aspect, vegetation type, naturalness, coverage of various vegetation layers, and source dataset, plot-level data also include community-weighted means and variances of 18 plant functional traits from the TRY Plant Trait Database. Spatial location and grain: Global, 0.01?40,000 m². Time period and grain: 1888-2015, recording dates. Major taxa and level of measurement: 42,677 vascular plant taxa, plot-level records.Fil: Sabatini, Francesco Maria. Martin-universität Halle-wittenberg; Alemania. German Centre For Integrative Biodiversity Research (idiv) Halle-jena-leipzig; AlemaniaFil: Lenoir, Jonathan. Université de Picardie Jules Verne; FranciaFil: Hattab, Tarek. Université de Montpellier; FranciaFil: Arnst, Elise Aimee. Manaaki Whenua - Landcare Research; Nueva ZelandaFil: Chytrý, Milan. Masaryk University; República ChecaFil: Giorgis, Melisa Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Vanselow, Kim André. University of Erlangen-Nuremberg; AlemaniaFil: Vásquez Martínez, Rodolfo. Jardín Botánico de Missouri Oxapampa; PerúFil: Vassilev, Kiril. Bulgarian Academy of Sciences; BulgariaFil: Vélez-Martin, Eduardo. ILEX Consultoria Científica; BrasilFil: Venanzoni, Roberto. University of Perugia; ItaliaFil: Vibrans, Alexander Christian. Universidade Regional de Blumenau; BrasilFil: Violle, Cyrille. Paul Valéry Montpellier University; FranciaFil: Virtanen, Risto. German Centre for Integrative Biodiversity Research; AlemaniaFil: von Wehrden, Henrik. Leuphana University of Lüneburg; AlemaniaFil: Wagner, Viktoria. University of Alberta; CanadáFil: Walker, Donald A.. University of Alaska; Estados UnidosFil: Waller, Donald M.. University of Wisconsin-Madison; Estados UnidosFil: Wang, Hua-Feng. Hainan University; ChinaFil: Wesche, Karsten. Senckenberg Museum of Natural History Görlitz; Alemania. Technische Universität Dresden; AlemaniaFil: Whitfeld, Timothy J. S.. University of Minnesota; Estados UnidosFil: Willner, Wolfgang. University of Vienna; AustriaFil: Wiser, Susan K.. Manaaki Whenua. Landcare Research; Nueva ZelandaFil: Wohlgemuth, Thomas. Swiss Federal Institute for Forest, Snow and Landscape Research; SuizaFil: Yamalov, Sergey. Russian Academy of Sciences; RusiaFil: Zobel, Martin. University of Tartu; EstoniaFil: Bruelheide, Helge. German Centre for Integrative Biodiversity Research; Alemani

Mapping species richness of plant families in European vegetation

Aims: Biodiversity is traditionally studied mostly at the species level, but biogeographical and macroecological studies at higher taxonomic levels can provide valuable insights into the evolutionary processes at large spatial scales. Our aim was to assess the representation of vascular plant families within different vegetation formations across Europe. Location: Europe. Methods: We used a data set of 816,005 vegetation plots from the European Vegetation Archive (EVA). For each plot, we calculated the relative species richness of each plant family as the number of species belonging to that family divided by the total number of species. We mapped the relative species richness, averaged across all plots in 50 km × 50 km grid cells, for each family and broad habitat groups: forests, grasslands, scrub and wetlands. We also calculated the absolute species richness and the Shannon diversity index for each family. Results: We produced 522 maps of mean relative species richness for a total of 152 vascular plant families occurring in forests, grasslands, scrub and wetlands. We found distinct spatial patterns for many combinations of families and habitat groups. The resulting series of 522 maps is freely available, both as images and GIS layers. Conclusions: The distinct spatial patterns revealed in the maps suggest that the relative species richness of plant families at the community level reflects the evolutionary history of individual families. We believe that the maps and associated data can inspire further biogeographical and macroecological studies and strengthen the ongoing integration of phylogenetic, functional and taxonomic diversity concepts.MV, IA, JPC, ZL, IK, AJ and MC were funded by the Czech Science Foundation, programme EXPRO (project no. 19-28491X); JDi by the Czech Science Foundation (18-02773S); IB and JAC by the Basque Government (IT936-16); AČ by the Slovenian Research Agency (ARRS, P1-0236); AK by the National Research Foundation of Ukraine (project no. 2020.01/0140); JŠ by the Slovak Research and Development Agency (APVV 16-0431); KV by the National Science Fund (Contract DCOST 01/7/19.10.2018)

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

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

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

European Vegetation Archive (EVA): An integrated database of European vegetation plots

© 2016 International Association for Vegetation Science. 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