EuroSL – a European taxonomic backbone for vegetation databases and other taxon- related databases: version 1.0

Background: A taxonomic reference list is an indispensable tool to sample, manage and match biodiversity data from different sources. Merging vegetation databases or combining them with taxon-related attributes needs reliable and consistent information about the taxon concepts used and an appropriate naming. Aim: Creating a “taxonomic backbone” of European vascular plants and bryophytes with links to widespread taxonomic references. Methods: We used the Euro+Med plant list (Euro+Med 2006ff), version 2015/04. For all families not yet covered there we used taxa from Flora Europaea (Tutin et al. 1980ff). Additionally we included the aggregates from the Ehrendorfer (1973) list. For bryophytes we rely on Grolle & Long (2000) and Hill et al. (2006). Results: EuroSL 1.0 covers > 45T accepted taxa and >77T synonyms from approx. 370 families. At the species level this means approx. 32T accepted names and >44T synonyms. EuroSL list will be published open access to allow referencing and connecting taxon-related databases beyond country borders. Future releases of EuroSL might contain additional taxonomic groups (algae and lichens), aggregates or new names as needed. However, a thorough documentation and transparency regarding taxon concepts, i.e. name usage = taxon circumscription, given by citing the source lists, will remain the highest priority. The first application of EuroSL will be the compilation of Ecological Indicator Values for Europe (EIVE version 1.0)

Limonka Gmelinova (Limonium gmelinii) na dálnicích České republiky

The paper describes finds of Limonium gmelinii on the D1 and D2 motorways in southern Moravia (south-eastern part of the Czech Republic). Limonium gmelinii is a plant of continental halophytic vegetation, such as saline steppes and marshlands, with a large distribution range extending from south-eastern Europe over southern Ukraine, south-eastern European Russia to southern Siberia in the east and some parts of Central Asia in the south. It is reported here for the first time as a naturalized alien species of the Czech flora. Until now it has been known from four sites, of which one is situated on the D1 motorway near the village of Ostrovačice, west of the city of Brno (first recorded by J. Danihelka in 2009), and three on the D2 motorway southeast of Brno, near the villages of Otmarov, Opatovice and Rakvice (first recorded by P. Kocián in 2013 but already recognizable on Street View photographs of Google Maps from August 2009). The Ostrovačice site, harbouring a single specimen, is the only place where L. gmelinii is found at the road verge under steel beam barriers. At the remaining three sites, in contrast, plants are always found in the central reservation. While the population near Rakvice consists of up to 40 flowering specimens, the populations near Opatovice and Otmarov are less numerous, consisting of about 5 and 2 flowering individuals, respectively. The identification of our specimens (deposited at BRNU and OL) as L. gmelinii seems to be almost certain; however, we refrained from identification to the microspecies level (as L. hungaricum or L. hypanicum, both described by M. V. Klokov) because the infraspecific variation of L. gmelinii is insufficiently known. We assume that the seeds of L. gmelinii were introduced to the Czech motorways via international traffic from Hungary but this assumption is based solely on geographic considerations and cannot be supported by any other arguments. This may have happened as soon as in the early 2000s, and now the species seems to be naturalized and about to spread. However, it will most likely remain confined to motorway central reservations and verges of main roads because of its specific habitat requirements

Naturalization of central European plants in North America: species traits, habitats, propagule pressure, residence time

The factors that promote invasive behavior in introduced plant species occur across many scales of biological and ecological organization. Factors that act at relatively small scales, for example, the evolution of biological traits associated with invasiveness, scale up to shape species distributions among different climates and habitats, as well as other characteristics linked to invasion, such as attractiveness for cultivation (and by extension propagule pressure). To identify drivers of invasion it is therefore necessary to disentangle the contribution of multiple factors that are interdependent. To this end, we formulated a conceptual model describing the process of invasion of central European species into North America based on a sequence of ‘‘drivers.’’ We then used confirmatory path analysis to test whether the conceptual model is supported by a statistical model inferred from a comprehensive database containing 466 species. The path analysis revealed that naturalization of central European plants in North America, in terms of the number of North American regions invaded, most strongly depends on residence time in the invaded range and the number of habitats occupied by species in their native range. In addition to the confirmatory path analysis, we identified the effects of various biological traits on several important drivers of the conceptualized invasion process. The data supported a model that included indirect effects of biological traits on invasion via their effect on the number of native range habitats occupied and cultivation in the native range. For example, persistent seed banks and longer flowering periods are positively correlated with number of native habitats, while a stress-tolerant life strategy is negatively correlated with native range cultivation. However, the importance of the biological traits is nearly an order of magnitude less than that of the larger scale drivers and highly dependent on the invasion stage (traits were associated only with native range drivers). This suggests that future research should explicitly link biological traits to the different stages of invasion, and that a failure to consider residence time or characteristics of the native range may seriously overestimate the role of biological traits, which, in turn, may result in spurious predictions of plant invasiveness

Classification of the Mediterranean lowland to submontane pine forest vegetation

Vegetation SurveyAim: Vegetation types of Mediterranean thermophilous pine forests dominated by Pinus brutia, Pinus halepensis, Pinus pinaster and Pinus pinea were studied in various areas. However, a comprehensive formal vegetation classification of these forests based on a detailed data analysis has never been developed. Our aim is to provide the first broad-scale classification of these pine forests based on a large data set of vegetation plots. Location: Southern Europe, North Africa, Levant, Anatolia, Crimea and the Caucasus. Methods: We prepared a data set of European and Mediterranean pine forest vegetation plots. We selected 7,277 plots dominated by the cold-sensitive Mediterranean pine species Pinus brutia, Pinus halepensis, Pinus pinaster and Pinus pinea. We classified these plots using TWINSPAN, interpreted the ecologically and biogeographically homogeneous TWINSPAN clusters as alliances, and developed an expert system for automatic vegetation classification at the class, order and alliance levels. Results: We described Pinetea halepensis as a new class for the Mediterranean lowland to submontane pine forests, included in the existing Pinetalia halepensis order, and distinguished 12 alliances of native thermophilous pine forests, including four newly described and three informal groups merging supposedly native stands and old-established plantations. The main gradients in species composition reflect elevational vegetation belts and the west–east, and partly north–south, biogeographical differences. Both temperature and precipitation seasonality co-vary with these gradients. Conclusions: We provide the first formal classification at the order and alliance levels for all the Mediterranean thermophilous pine forests based on vegetation-plot data. This classification includes traditional syntaxa, which have been critically revised, and a new class and four new alliances. We also outline a methodological workflow that might be useful for other vegetation classification syntheses. The expert system, which is jointly based on pine dominance and species composition, is a tool for applying this classification in research and nature conservation survey, monitoring and managementinfo:eu-repo/semantics/publishedVersio

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

TRY plant trait database - enhanced coverage and open access

This article has 730 authors, of which I have only listed the lead author and myself as a representative of University of HelsinkiPlant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.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

TRY plant trait database - enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives