Catalogue of alien plants of the Czech Republic (2nd edition): checklist update, taxonomic diversity and invasion patterns

A complete list of all alien taxa ever recorded in the flora of the Czech Republic is presented as an update of the original checklist published in 2002. New data accumulated in the last decade are incorporated and the listing and status of some taxa are reassessed based on improved knowledge. Alien flora of the Czech Republic consists of 1454 taxa listed with information on their taxonomic position, life history, geographic origin (or mode of origin, distinguishing anecophyte and hybrid), invasive status (casual; naturalized but not invasive; invasive), residence time status (archaeophyte vs neophyte), mode of introduction into the country (accidental, deliberate), and date of the first record. Additional information on species performance that was not part of the previous catalogue, i.e. on the width of species’ habitat niches, their dominance in invaded communities, and impact, is provided. The Czech alien flora consists of 350 (24.1%) archaeophytes and 1104 (75.9%) neophytes. The increase in the total number of taxa compared to the previous catalogue (1378) is due to addition of 151 taxa and removal of 75 (39 archaeophytes and 36 neophytes), important part of the latter being the reclassification of 41 taxa as native, mostly based on archaeobotanical evidence. The additions represent taxa newly recorded since 2002 and reported in the national literature; taxa resulting from investigation of sources omitted while preparing the previous catalogue; redetermination of previously reported taxa; reassessment of some taxa traditionally considered native for which the evidence suggests the opposite; and inclusion of intraspecific taxa previously not recognized in the flora. There are 44 taxa on the list that are reported in the present study for the first time as aliens introduced to the Czech Republic or escaped from cultivation.Práce přináší úplný seznam nepůvodních taxonů zaznamenaných na území České republiky; je aktualizací a doplněním předchozího seznamu publikovaného v roce 2002. Zahrnuje nové údaje shromážděné za poslední desetiletí a přehodnocuje zařazení a status některých druhů, vyplývající z rozvoje taxonomického poznání. Nepůvodní flóra České republiky zahrnuje 1454 taxonů, které jsou uvedeny v Apendixu 2 s informacemi o taxonomické příslušnosti, životní formě, oblasti původu, invazním statusu (zda jde o druh přechodně zavlečený, naturalizovaný avšak neinvazní, nebo invazní), charakteru výskytu v krajině, době zavlečení (archeofyt nebo neofyt), způsobu introdukce do země a u neofytů o datu prvního nálezu. Oproti původnímu katalogu je uveden počet typů biotopů, ve kterých se druh vyskytuje, pokryvnost v rostlinných společenstvech a impakt. Podíl zavlečených druhů v české flóře je značný: tvoří jej 350 (24,1%) archeofytů a 1104 (75.9%) neofytů. Nárůst počtu taxonů oproti původnímu katalogu, který uváděl 1378 taxonů, vyplývá z toho, že bylo přidáno 151 taxonů. Celkem 75 (39 archeofytů a 36 neofytů) bylo naproti tomu vypuštěno; značná část tohoto počtu jde na vrub přeřazení 41 taxonů mezi původní druhy, a to vesměs na základě archeobotanických dokladů. Přírůstky na seznamu představují taxony nově objevené a uvedené v botanické literatuře od roku 2002, taxony zařazené na základě excerpce dříve opominutých zdrojů či revize zdrojů použitých, nebo přehodnocení statusu některých taxonů tradičně považovaných za původní. Vněkterých případech jde o infraspecifické taxony, které nebyly dříve v české flóře rozeznávány. Seznam obsahuje 44 taxonů, které jsou uváděny pro Českou republiku poprvé jako zavlečené, nebo pro něž je podán první důkaz o jejich zplaňování

Vegetation of Europe: hierarchical floristic classification system of vascular plant, bryophyte, lichen, and algal communities

Vegetation classification consistent with the Braun-Blanquet approach is widely used in Europe for applied vegetation science, conservation planning and land management. During the long history of syntaxonomy, many concepts and names of vegetation units have been proposed, but there has been no single classification system integrating these units. Here we (1) present a comprehensive, hierarchical, syntaxonomic system of alliances, orders and classes of Braun-Blanquet syntaxonomy for vascular plant, bryophyte and lichen, and algal communities of Europe; (2) briefly characterize in ecological and geographic terms accepted syntaxonomic concepts; (3) link available synonyms to these accepted concepts; and (4) provide a list of diagnostic species for all classes. Location: European mainland, Greenland, Arctic archipelagos (including Iceland, Svalbard, Novaya Zemlya), Canary Islands, Madeira, Azores, Caucasus, Cyprus. Methods: We evaluated approximately 10 000 bibliographic sources to create a comprehensive list of previously proposed syntaxonomic units. These units were evaluated by experts for their floristic and ecological distinctness, clarity of geographic distribution and compliance with the nomenclature code. Accepted units were compiled into three systems of classes, orders and alliances (EuroVegChecklist, EVC) for communities dominated by vascular plants (EVC1), bryophytes and lichens (EVC2) and algae (EVC3). Results: EVC1 includes 109 classes, 300 orders and 1108 alliances; EVC2 includes 27 classes, 53 orders and 137 alliances, and EVC3 includes 13 classes, 24 orders and 53 alliances. In total 13 448 taxa were assigned as indicator species to classes of EVC1, 2087 to classes of EVC2 and 368 to classes of EVC3. Accepted syntaxonomic concepts are summarized in a series of appendices, and detailed information on each is accessible through the software tool EuroVegBrowser. Conclusions: This paper features the first comprehensive and critical account of European syntaxa and synthesizes more than 100 yr of classification effort by European phytosociologists. It aims to document and stabilize the concepts and nomenclature of syntaxa for practical uses, such as calibration of habitat classification used by the European Union, standardization of terminology for environmental assessment, management and conservation of nature areas, landscape planning and education. The presented classification systems provide a baseline for future development and revision of European syntaxonomy.info:eu-repo/semantics/publishedVersio

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

Semi-supervised classification of vegetation: preserving the good old units and searching for new ones

AimThe unsupervised nature of traditional numerical methods used to classify vegetation hinders the development of comprehensive vegetation classification systems. Each new unsupervised classification yields partitions that are partly inconsistent with previous classifications and change group membership for some sites. In contrast, supervised methods account for previously established vegetation units, but cannot define new ones. Therefore, we introduce the concept of semi-supervised classification to community ecology and vegetation science. Semi-supervised classification formally reproduces the existing units in a supervised mode and simultaneously identifies new units among unassigned sites in an unsupervised mode. We discuss the concept of semi-supervised clustering, introduce semi-supervised variants of two clustering algorithms that produce groups with crisp boundaries, k-means and partitioning around medoids (PAM), provide a free software tool to perform these classifications and demonstrate the advantages using example data sets of vegetation plots. MethodsSemi-supervised methods use a priori information about group membership for some sites to define centroids (k-means) or medoids (PAM) of site groups that represent previously established vegetation units. They identify these groups in a species hyperspace and assign new sites to them. At the same time, they search for a user-defined number of new groups. We compared the unsupervised, supervised and semi-supervised methods using an example of a forest vegetation data set that was previously classified using expert knowledge, and assessed how well these methods reproduced vegetation units defined by experts. Then we compared supervised and semi-supervised methods in a task when a grassland vegetation classification established in one country was extended to two neighbouring countries. Results and conclusionsExample analyses of vegetation plot data sets demonstrated that semi-supervised variants of k-means and PAM are extremely valuable tools for extending existing vegetation classifications while preserving previously defined vegetation units. They can be used both for identifying so far unrecognized vegetation types in the regions where a vegetation classification already exists and for extending a vegetation classification from a particular region to neighbouring regions with partly identical but partly different vegetation types. Both k-means and PAM provide site groups with crisp boundaries, which makes them a simpler alternative to fuzzy clustering methods

Biotic homogenization of urban floras by alien species. The role of species turnover and richness differences

Question: The spread of alien species has been changing the diversity of plant communities all over the world, perhaps most notably in urban habitats. It has been shown that alien species with different residence times have different impacts on the b-diversity of urban plant communities: archaeophytes tend to contribute to homogenization, while neophytes tend to increase differentiation among sites. However, it has not been determined whether these processes result from changes in species turnover or from differences in species richness. Here, we use an additive partitioning framework to disentangle the contribution of species turnover and richness difference to b-diversity patterns in invaded urban plant communities. Location: Thirty-two cities in ten countries of Central Europe and Benelux. Methods:We analysed the effects of alien species on b-diversity of urban plant communities separately for archaeophytes and neophytes to assess whether the observed patterns differ between these two groups of species with different residence times in the invaded region. We used additive as well as non-additive measures of species turnover and richness difference. For this purpose, we proposed a new index that complements the recently proposed Podani-Schmera index of richness difference. Results: We confirmed the results of earlier studies that neophytes tend to differentiate the urban plant communities, while archaeophytes tend to homogenize, although in some specific habitats they can also contribute to differentiation. The observed changes in b-diversity were related to the turnover component of b-diversity in most cases, especially for neophytes. In contrast, the richness difference component was not significantly different between neophytes and native species. The trends for archaeophytes were less consistent, but inmost habitats their turnover and richness difference were not significantly different fromnative species. Conclusions: Changes in b-diversity of urban plant communities induced by the establishment of alien species reflect mainly species turnover, whereas the richness difference component has small effects restricted to certain habitats only