Flavia Landucci

VegItaly: The Italian collaborative project for a national vegetation database

Abstract Two years after its official start, the national vegetation database VegItaly, a collaborative project supported by the Italian scientific community and developed by a large group of scientists, is presented. This article offers a concise overview of the content of the database, currently consisting of 31,100 vegetation plot, including published and unpublished data. Some basic statistics are analysed; for example, data distribution in space and time, represented vegetation types expressed as physiognomic categories. Although rather young and still in progress, VegItaly already contains data from all the Italian regions and stands as an optimal candidate for the development of an Italian national vegetation database. Its main goals,theoretical basis, technical features, functionalities and recent progresses are outlined, showing glimpses of future prospects.

New occurrence of reed bed decline in southern Europe: Do permanent flooding and chemical parameters play a role?

Based on the experimental design proposed in similar studies, macromorphological and ecological traits of common reed beds were analysed at Lake Chiusi (Central Italy), together with selected chemical parameters in sediments and interstitial waters and aerial images of the site, in order to investigate reed decline and search for possible correlations among data. Typical symptoms of the reed dieback syndrome were detected, thus enlarging the occurrence of this phenomenon in southern Europe. Permanently dry, permanently flooded and partially flooded stands show different levels of decline, with the permanent flooding always co-occurring with reed dieback. Only few of the considered chemical parameters seem to play a role in reed decline (nitrates, rubidium, nickel, barium, manganese), although no clear pattern was identified. Data suggest that the co-occurrence of some chemicals with stressing conditions might affect the growth even of an efficient metal accumulator, as reed is generally considered.

Cyto-histological and morpho-physiological responses of common duckweed (Lemna minor L.) to chromium

Along with cadmium, lead, mercury and other heavy metals, chromium is an important environmental pollutant, mainly concentrated in areas of intense anthropogenic pressure. The effect of potassium dichromate on Lemna minor populations was tested using the growth inhibition test. Cyto-histological and physiological analyses were also conducted to aid in understanding the strategies used by plants during exposure to chromium. Treatment with potassium dichromate caused a reduction in growth rate and frond size in all treated plants and especially at the highest concentrations. At these concentrations the photosynthetic pathway was also altered as shown by the decrease of maximum quantum yield of photosystem II and the chlorophyll b content and by the chloroplast ultrastructural modifications. Starch storage was also investigated by microscopic observations. It was the highest at the high concentrations of the pollutant. The data suggested a correlation between starch storage and reduced growth; there was greater inhibition of plant growth than inhibition of photosynthesis, resulting in a surplus of carbohydrates that may be stored as starch. The investigation helps to understand the mechanism related to heavy metal tolerance of Lemna minor and supplies information about the behavior of this species widely used as a biomarker.

The anArchive taxonomic Checklist for Italian botanical data banking and vegetation analysis: Theoretical basis and advantages

In recent years, research in botany was increasingly related with the use of large data-sets and data banks, in order to address emerging issues such as the severe risk of species, habitats and biodiversity loss. In this frame, the anArchive taxonomic Checklist, an online synonymized list of botanical species names, developed to support the botanical data banking and vegetation analysis, is presented and discussed here. The benefits deriving from such a supervised and referenced tool are emphasized. They include the possibility to keep track of old and new species names, pointing out the latest reviewed accepted scientific name and its synonyms, and harmonizing different taxonomic points of view. Furthermore, the list is open access and expert qualified customers can collaborate to its improvement. The basic unit of the taxonomic Checklist is an object including the taxon name at specific or, when present, infraspecific level; the taxonomic frame stops at the level of family and ranks higher than genus are not treated hierarchically. Some technical features, the main taxonomic references and the current state of the art are reported.

European Red List of Habitats : Part 2. Terrestrial and freshwater habitats

The first ever European Red List of Habitats reviews the current status of all natural and semi-natural terrestrial, freshwater and marine habitats and highlights the pressures they face. Using a modified version of the IUCN Red List of Ecosystems categories and criteria, it covers the EU28, plus Iceland, Norway, Switzerland and the Balkan countries and their neighbouring seas. Over 230 terrestrial and freshwater habitats were assessed. The European Red List of Habitats provides an entirely new and all embracing tool to review commitments for environmental protection and restoration within the EU2020 Biodiversity Strategy. In addition to the assessment of threat, a unique set of information underlies the Red List for every habitat: from a full description to distribution maps, images, links to other classification systems, details of occurrence and trends in each country and lists of threats with information on restoration potential. All of this is publicly available in PDF and database format (see links below), so the Red List can be used for a wide range of analysis. The Red List complements the data collected on Annex I habitat types through Article 17 reporting as it covers a much wider set of habitats than those legally protected under the Habitats Directive.

Formalized classification of European fen vegetation at the alliance level

Phytosociological classification of fen vegetation (Scheuchzerio palustris-Caricetea fuscae class) differs among European countries. Here we propose a unified vegetation classification of European fens at the alliance level, provide unequivocal assignment rules for individual vegetation plots, identify diagnostic species of fen alliances, and map their distribution. 29 049 vegetation-plot records of fenswere selected fromdatabases using a list of specialist fen species. Formal definitions of alliances were created using the presence, absence and abundance of Cocktail-based species groups and indicator species. DCA visualized the similarities among the alliances in an ordination space. The ISOPAM classification algorithm was applied to regional subsets with homogeneous plot size to check whether the classification based on formal definitions matches the results of unsupervised classifications. The following alliances were defined: Caricion viridulo-trinervis (sub-halophytic Atlantic dune-slack fens), Caricion davallianae (temperate calcareous fens), Caricion atrofusco-saxatilis (arcto-alpine calcareous fens), Stygio-Caricion limosae (boreal topogenic brown-moss fens), Sphagno warnstorfii-Tomentypnion nitentis (Sphagnumbrown-moss rich fens), Saxifrago-Tomentypnion (continental to boreo-continental nitrogen-limited brown-moss rich fens), Narthecion scardici (alpine fens with Balkan endemics), Caricion stantis (arctic brown-moss rich fens), Anagallido tenellae-Juncion bulbosi (Ibero-Atlantic moderately rich fens), Drepanocladion exannulati (arcto-borealalpine non-calcareous fens), Caricion fuscae (temperate moderately rich fens), Sphagno-Caricion canescentis (poor fens) and Scheuchzerion palustris (dystrophic hollows). The main variation in the species composition of European fens reflected site chemistry (pH, mineral richness) and sorted the plots from calcareous and extremely rich fens, through rich andmoderately rich fens, to poor fens and dystrophic hollows.

The Magnocaricetalia Pignatti 1953 ( Phragmito-Magnocaricetea Klika in Klika et Novák 1941) Plant Communities of Italy

The Authors present a list of marshy and wet vegetation described or present in Italy included in the Magnocaricetalia Pignati 1953 Order (Phragmito-Magnocaricetea Klika in Klika et Novak 1941 Class) focusing on nomenclatural, floristic, physiognomic and ecological traits reported by Authors in the examined literature.

Joint optimization of cluster number and abundance transformation for obtaining effective vegetation classifications

Question: Is it possible to determine which combination of cluster number and taxon abundance transformation would produce the most effective classification of vegetation data? What is the effect of changing cluster number and taxon abundance weighting (applied simultaneously) on the stability and biological interpretation of vegetation classifications? Locality: Europe, Western Australia, simulated data. Methods: Real data sets representing Hungarian sub-montane grasslands, European wetlands, and Western Australian kwongan vegetation, as well as simulated data sets were used. The data sets were classified using the partitioning around medoids method. We generated classification solutions by gradually changing the transformation exponent applied to the species projected covers and the number of clusters. The effectiveness of each classification was assessed with a stability index. This index is based on bootstrap resampling of the original data set with subsequent elimination of duplicates. The vegetation types delimited by the most stable classification were compared with other classifications obtained at local maxima of the stability values. The effect of changing the transformation power exponent on the number of clusters, indexed according to their stability, was evaluated. Results: The optimal number of clusters varied with the power exponent in all cases, both with real and simulated data sets. With the real data sets, optimal cluster numbers obtained with different data transformations recovered interpretable biological patterns. Using the simulated data, the optima of stability values identified the simulated number of clusters correctly in most cases. Conclusions: With changing the settings of data transformation and the number of clusters, classifications of different stability can be produced. Highly stable classifications can be obtained from different settings for cluster number and data transformation. Despite similarly high stability, such classifications may reveal contrasting biological patterns, thus suggesting different interpretations. We suggest testing a wide range of available combinations to find the parameters resulting in the most effective classifications.