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Human-assisted introduction of alien plants is causing ecosystem transformations worldwide and is considered an important threat to biodiversity. We provide a European assessment of habitat levels of invasion in heathlands and scrub and identify successful alien plants and invasion trends across biogeographical regions. We analysed a geographically stratified data set of 24,220 dwarf shrub and scrub vegetation plots sampled across Europe. Among the 6547 vascular plant taxa occurring in these plots, we identified 311 neophytes (4.8%, i.e. alien species introduced in Europe or its sub-regions after 1500 AD) and compared five metrics of the level of invasion in (i) EUNIS habitats, (ii) broad habitat groups and (iii) biogeographical regions of Europe. We related habitat-specific levels of invasion to elevation and climatic variables using generalized linear models. Among neophytes, phanerophytes of non-European origin prevailed. The most frequent neophytes in the plots were Prunus serotina, Robinia pseudoacacia and Quercus rubra among phanerophytes, Impatiens parviflora among therophytes, and Erigeron canadensis and Solidago gigantea among hemicryptophytes. Levels of invasion significantly differed among habitats and biogeographical regions. The most invaded habitat was Macaronesian lowland scrub, followed by riparian scrub, Rubus scrub and forest-clearing scrub of temperate Europe, and coastal dune scrub of the Atlantic region. The levels of invasion were low in the shrublands of the Arctic and Mediterranean regions and decreased with elevation within habitats. Results suggest that insularity, low elevation, frequent disturbances, and high availability or considerable fluctuation of resources promote neophyte invasions in European shrublands. [ABSTRACT FROM AUTHOR]

Earth observation (EO) data, derived from remote sensing and unmanned aerial vehicle (UAV), have been recently demonstrated to be essential tools for the ecosystem monitoring and habitat mapping, combining high technological and methodological procedures for applied ecology. However, research based on EO data analyses often tend to focus on image processing techniques, neglecting the development of a detailed sampling design scheme needed for an exhaustive habitat detection. This paper shows the results of a novel approach for mapping coastal dune habitats at a fine scale, using a supervised machine learning model, through the combination of vegetation plot sampling scheme, synergic use of multi‐sensor spectral imagery (UAV‐VHR) and environmental predictors (e.g., LiDAR), object‐based image analysis, and landscape metrics analysis. Proposed approach was tested in a protected area, established to preserve notable habitats along the Italian Tyrrhenian coast. A detailed sampling scheme was designed and carried out during spring and summer of 2019, combining simultaneously UAV flight acquisition and field vegetation survey data, collected at high precision positioning. The calibrated classification model achieved an overall accuracy of 78.6% (standard error 4.33), allowing us to accurately classify and map five coastal habitats, according to EUNIS (European Nature Information System) classification, which were further verified through a fully independent validation field survey. Results demonstrate that VHR imageries, combined with specific field survey schemes, can be exploited to train classification models used for the detection of plant communities (i.e., meso‐habitat) and plant species at local scale. Our findings demonstrate that UAV‐VHR data is a valid tool to produce high spatial resolution information in sand beach ecosystems, giving ecology research a new way for responsive, timely, and cost‐effective ecosystem monitoring. Earth observation (EO) data, derived from remote and proximal sensing (e.g. via Unmanned Aerial Vehicles – UAVs), have been recently demonstrated to be essential tools for ecosystem monitoring and habitat mapping, combining high technological and methodological procedures for applied ecology. Research based on EO data analyses often tend to focus on images processing techniques, neglecting the development of a detailed sampling design scheme needed for an exhaustive habitat detection. This paper shows the results of a novel approach for mapping coastal dune habitats at a local scale, using a supervised machine learning model, through the combination of a vegetation plot sampling scheme, synergic use of multi‐sensor spectral imagery (UAV‐VHR) and environmental predictors (e.g. LiDAR), Object‐Based Image Analysis and landscape metrics analysis. Our findings demonstrate that UAV‐VHR data are a valid tool to produce high spatial resolution information in sandy beach ecosystems, giving ecology research a new way of responsive, timely, and cost‐effective ecosystem monitoring. [ABSTRACT FROM AUTHOR]

Aim: Assessing the performances of different sampling approaches for documenting community diversity may help to identify optimal sampling efforts and strategies, and to enhance conservation and monitoring planning. Here, we used two data sets based on probabilistic and preferential sampling schemes of Italian forest vegetation to analyze the multifaceted performances of the two approaches across three major forest types at a large scale. Location: Italy. Methods: We pooled 804 probabilistic and 16,259 preferential forest plots as samples of vascular plant diversity across the country. We balanced the two data sets in terms of sizes, plot size, geographical position, and vegetation types. For each of the two data sets, 1000 subsets of 201 random plots were compared by calculating the shared and exclusive indicator species, their overlap in the multivariate space, and the areas encompassed by spatially‐constrained rarefaction curves. We then calculated an index of performance using the ratio between the additional and total information collected by each sampling approach. The performances were tested and evaluated across the three major forest types. Results: The probabilistic approach performed better in estimating species richness and diversity of species assemblages, but did not detect other components of the regional diversity, such as azonal forests. The preferential approach outperformed the probabilistic approach in detecting forest‐specialist species and plant diversity hotspots. Conclusions: Using a novel workflow based on vegetation‐plot exclusivities and commonalities, our study suggests probabilistic and preferential sampling approaches are to be used in combination for better conservation and monitor planning purposes to detect multiple aspects of plant community diversity. Our findings can assist the implementation of national conservation planning and large‐scale monitoring of biodiversity. [ABSTRACT FROM AUTHOR]

The obtained procedures could be applied on several environmental data in order to cyclically and promptly repeat spatial analysis to detect changes in space and time in support of ecosystem conservation issues, especially to evaluate the impact of illegal actions (e.g., forest harvesting) or natural hazards (e.g., destructive storms or other natural disasters) on habitat distribution. Among the methods used to process the remotely sensed data, the increasing focus on the use of machine learning algorithm models such as Random Forests (RF), Support Vector Machine (SVM), Linear Regression (LR), Convolutional Neural Network (CNN), and Deep Learning (DL) classifier is noteworthy. We hope that the results and findings shown here will encourage further research and the land managers of the importance and benefits of better integration of remote sensing data on operational monitoring and surveillance of ecosystems. Recent global-scale environmental issues from climate change to biodiversity loss are generating an intense social pressure on the scientific community [[1]]. [Extracted from the article]

Habitat richness, that is, the diversity of ecosystem types, is a complex, spatially explicit aspect of biodiversity, which is affected by bioclimatic, geographic, and anthropogenic variables. The distribution of habitat types is a key component for understanding broad‐scale biodiversity and for developing conservation strategies. We used data on the distribution of European Union (EU) habitats to answer the following questions: (i) how do bioclimatic, geographic, and anthropogenic variables affect habitat richness? (ii) Which of those factors is the most important? (iii) How do interactions among these variables influence habitat richness and which combinations produce the strongest interactions? The distribution maps of 222 terrestrial habitat types as defined by the Natura 2000 network were used to calculate habitat richness for the 10 km × 10 km EU grid map. We then investigated how environmental variables affect habitat richness, using generalized linear models, generalized additive models, and boosted regression trees. The main factors associated with habitat richness were geographic variables, with negative relationships observed for both latitude and longitude, and a positive relationship for terrain ruggedness. Bioclimatic variables played a secondary role, with habitat richness increasing slightly with annual mean temperature and overall annual precipitation. We also found an interaction between anthropogenic variables, with the combination of increased landscape fragmentation and increased population density strongly decreasing habitat richness. This is the first attempt to disentangle spatial patterns of habitat richness at the continental scale, as a key tool for protecting biodiversity. The number of European habitats is related to geography more than climate and human pressure, reflecting a major component of biogeographical patterns similar to the drivers observed at the species level. The interaction between anthropogenic variables highlights the need for coordinated, continental‐scale management plans for biodiversity conservation. [ABSTRACT FROM AUTHOR]

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Aims: (a) To determine the contribution of current macro‐environmental factors in explaining the phylogenetic structure of European forest vegetation, (b) to map and describe spatial patterns in their phylogenetic structure and (c) to examine which lineages are the most important contributors to phylogenetic clustering and whether their contribution varies across forest types and regions. Location: Europe. Taxon: Angiosperms. Methods: We analysed the phylogenetic structure of 61,816 georeferenced forest vegetation plots across Europe considering alternative metrics either sensitive to basal (ancient evolutionary dynamics) or terminal (recent dynamics) branching in the phylogeny. We used boosted regression trees to model metrics of the phylogenetic structure as a function of current macro‐environmental factors. We also identified clades encompassing significantly more taxa than under random expectation in phylogenetically clustered plots. Results: Phylogenetic clustering was driven by climatic stress and instability and was strong in the areas glaciated during the Pleistocene, likely reflecting limited postglacial migration, and to a lower extent in areas of northern‐central Europe and in summer‐dry Mediterranean regions. Phylogenetic overdispersion was frequent in the hemiboreal zone in Russia, in some areas around the Mediterranean Basin, and along the Atlantic seaboard of the Iberian Peninsula. The families Ericaceae, Poaceae and Fagaceae were overrepresented in clustered plots in different regions of Europe. Main conclusions: We provide the first maps and analyses on the phylogenetic structure of European forest vegetation at the plot level. Our results highlight the role of environmental filtering, postglacial dispersal limitation and spatial transitions between major biomes in determining the distribution of plant lineages in Europe. [ABSTRACT FROM AUTHOR]

Questions: European woodlands harbor at least 386 alien plant species but the factors driving local invasions remain unknown. By using a large vegetation‐plot database, we asked how local richness and abundance of alien species vary by regions, elevation, climate, soil properties, human disturbance, and habitat types. Location: Western, central and southern Europe. Methods: We linked consolidated data from the European Vegetation Archive (16,211 plots) to a habitat classification scheme, climate, soil properties and human disturbance variables. In addition, we used 250 km × 250 km regional grid cells to test whether local patterns differ among regions. We used generalized additive models (GAMs) and quantile GAMs to explore how relative alien species richness and the sum of alien species covers per plot relate to predictors. Random Forest analyses (RFs) were employed to assess the importance of individual predictors that were not multicollinear. Results: Relative alien species richness and the sum of alien species covers varied across regions and habitat types, with effects being more pronounced at the maximum rather than average responses. Both response variables declined with increasing elevation and distance to the nearest road or railroad and increased with the amount of sealed soil. Maxima in fitted functions matched plots from regional invasion hotspots in northwestern and central Europe. RFs accounted for 39.6% and 20.9% of the total variation in relative alien species richness and the sum of alien species covers, respectively, with region and habitat being the most important variables. Conclusions: The importance of maximum response quantiles and the prevalence of regional hotspots point to invasion debt in European woodlands. As alien plants expand further, their species richness and abundance in woodlands will be likely driven by the shared effects of the introduction and planting history, differences in the invaded habitat types, and dispersal corridors.Using a large vegetation‐plot data set from the European Vegetation Archive, we explored the relationship between the levels of alien plant invasion and environmental variables in European woodlands. Habitat types and regions emerged as the most important predictors, with the latter pointing to regional hotspots of local alien plant invasion and regional invasion debt in woodlands across Europe. [ABSTRACT FROM AUTHOR]

Aim: Vegetation types of Mediterranean thermophilous pine forests dominated by Pinusbrutia, Pinushalepensis, Pinuspinaster and Pinuspinea 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 Pinusbrutia, 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 Pineteahalepensis as a new class for the Mediterranean lowland to submontane pine forests, included in the existing Pinetaliahalepensis 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 management. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aim: To propose a Finite Mixture Model (FMM) as an additional approach for classifying large datasets of georeferenced vegetation plots from complex vegetation systems. Study area: The Italian peninsula including the two main islands (Sicily and Sardinia), but excluding the Alps and the Po plain. Methods: We used a database of 5,593 georeferenced plots and 1,586 vascular species of forest vegetation, created in TURBOVEG by storing published and unpublished phytosociological plots collected over the last 30 years. The plots were classified according to species composition and environmental variables using a FMM. Classification results were compared with those obtained by TWINSPAN algorithm. Groups were characterized in terms of ecological parameters, dominant and diagnostic species using the fidelity coefficient. Interpretation of resulting forest vegetation types was supported by a predictive map, produced using discriminant functions on environmental predictors, and by a non-metric multidimensional scaling ordination. Results: FMM clustering obtained 24 groups that were compared with those from TWINSPAN, and similarities were found only at a higher classification level corresponding to the main orders of the Italian broadleaf forest vegetation: Fagetalia sylvaticae, Carpinetalia betuli, Quercetalia pubescenti-petraeae and Quercetalia ilicis. At lower syntaxonomic level, these 24 groups were referred to alliances and sub-alliances. Conclusions: Despite a greater computational complexity, FMM appears to be an effective alternative to the traditional classification methods through the incorporation of modelling in the classificatory process. This allows classification of both the co-occurrence of species and environmental factors so that groups are identified not only on their species composition, as in the case of TWINSPAN, but also on their specific environmental niche. [ABSTRACT FROM AUTHOR]

Aim: The former continental‐scale studies modelled coarse‐grained plant species‐richness patterns (gamma diversity). Here we aim to refine this information for European forests by (a) modelling the number of vascular plant species that co‐occur in local communities (alpha diversity) within spatial units of 400 m2; and (b) assessing the factors likely determining the observed spatial patterns in alpha diversity. Location: Europe roughly within 12°W–30°E and 35–60°N. Taxon: Vascular plants. Methods: The numbers of co‐occurring vascular plant species were counted in 73,134 georeferenced vegetation plots. Each plot was classified by an expert system into deciduous broadleaf, coniferous or sclerophyllous forest. Random Forest models were used to map and explain spatial patterns in alpha diversity for each forest type separately using 19 environmental, land‐use and historical variables. Results: Our models explained from 51.0% to 70.9% of the variation in forest alpha diversity. The modelled alpha‐diversity pattern was dominated by a marked gradient from species‐poor north‐western to species‐rich south‐eastern Europe. The most prominent richness hotspots were identified in the Calcareous Alps and adjacent north‐western Dinarides, the Carpathian foothills in Romania and the Western Carpathians in Slovakia. Energy‐related factors, bedrock types and terrain ruggedness were identified as the main variables underlying the observed richness patterns. Alpha diversity increases especially with temperature seasonality in deciduous broadleaf forests, on limestone bedrock in coniferous forests and in areas with low annual actual evapotranspiration in sclerophyllous forests. Main conclusions: We provide the first predictive maps and analyses of environmental factors driving the alpha diversity of vascular plants across European forests. Such information is important for the general understanding of European biodiversity. This study also demonstrates a high potential of vegetation‐plot databases as sources for robust estimation of the number of vascular plant species that co‐occur at fine spatial grains across large areas. [ABSTRACT FROM AUTHOR]

Questions: European native laurophyllous species are interesting for their biogeography and evolutionary history, as well as for their responses to global changes. We identified Italian native laurophylls on the basis of morphology, biogeographical history and ecological niche to study whether these species are in equilibrium with the present‐day climate. Furthermore, we localized Quaternary refugia and areas of potential spread of native laurophylls using distribution patterns of realized and potential aggregations of native laurophylls, respectively. Location: Italy. Methods: We extracted data for 17 087 forest plots from a phytosociological database. Detrended Correspondence Analysis of these plots with overlay of climatic and environmental variables was performed to identify native laurophylls, i.e., evergreen broad‐leaved species of late Tertiary radiation, occupying a warm and humid niche. Potential ranges of laurophylls were calculated using Beals' index of sociological favourability. The realized/potential range‐size ratio was calculated for each single laurophyll to understand whether it is in equilibrium with the present‐day climate. Distribution patterns of realized and potential aggregations of laurophylls were mapped. Results: Ordination analysis provided a selection of 11 species as Italian native laurophylls. Most of them occupied less than half of their estimated potential range. Realized aggregations richest in native laurophylls were localized in the central Apennines. However, the Italian forests showed high potential for native laurophylls with the richest potential aggregations in the whole Apennines and in the southern Alps. Conclusions: Most of the Italian native laurophylls showed non‐equilibrium with the current climate, suggesting that late‐Quaternary events could better explain their present‐day distribution than on‐going climate change. Furthermore, their refugia in central Italy suggest the persistence in sites with temperate climate during Quaternary environmental changes. The high suitability of Italian forests for laurophylls, along with global changes, suggests possible future spreading of these species. [ABSTRACT FROM AUTHOR]

Abstract: Aim: We investigate whether (1) environmental predictors allow to delineate the distribution of discrete community types at the continental scale and (2) how data completeness influences model generalization in relation to the compositional variation of the modelled entities. Location: Europe. Methods: We used comprehensive datasets of two community types of conservation concern in Europe: acidophilous beech forests and base‐rich fens. We computed community distribution models (CDMs) calibrated with environmental predictors to predict the occurrence of both community types, evaluating geographical transferability, interpolation and extrapolation under different scenarios of sampling bias. We used generalized dissimilarity modelling (GDM) to assess the role of geographical and environmental drivers in compositional variation within the predicted distributions. Results: For the two community types, CDMs computed for the whole study area provided good performance when evaluated by random cross‐validation and external validation. Geographical transferability provided lower but relatively good performance, while model extrapolation performed poorly when compared with interpolation. Generalized dissimilarity modelling showed a predominant effect of geographical distance on compositional variation, complemented with the environmental predictors that also influenced habitat suitability. Main conclusions: Correlative approaches typically used for modelling the distribution of individual species are also useful for delineating the potential area of occupancy of community types at the continental scale, when using consistent definitions of the modelled entity and high data completeness. The combination of CDMs with GDM further improves the understanding of diversity patterns of plant communities, providing spatially explicit information for mapping vegetation diversity and related habitat types at large scales. [ABSTRACT FROM AUTHOR]

The conservation of habitat types has been recognized to be of relevant importance for the conservation of biodiversity and is a major concern in the European Union. With the 92/43/EEC Habitats Directive, the European Commission targeted these habitat types, which conservation must be ensured by Member States. In this context, the Habitat type 9330 “Quercus suber forests” is intended to ensure the conservation of cork oak woodlands in Europe. To support the enhancement of nature conservation policies, in this study we provide a classification of cork oak woodlands in Europe using a large vegetation database. We identify four major groups with clear biogeographic differences and characterize them by lists of indicator species. We also provide distribution maps based on occurrence data and the modelled potential area of distribution as an additional tool for conservation. This study offers a contribution to the comparative description of the European Q. suber woodlands subtypes and to establish a protocol for habitat monitoring and assessment. [ABSTRACT FROM AUTHOR]

Questions What are the main floristic patterns in European beech forests? Which classification at the alliance and suballiance level is the most convincing? Location Europe and Asia Minor. Methods We applied a TWINSPAN classification to a data set of 24 605 relevés covering the whole range of Fagus sylvatica forests and the western part of Fagus orientalis forests. We identified 24 'operational phytosociological units' (OPUs), which were used for further analysis. The position of each OPU along the soil pH and temperature gradient was evaluated using Ellenberg Indicator Values. Fidelity of species to OPUs was calculated using the phi coefficient and constancy ratio. We compared alternative alliance concepts, corresponding to groups of OPUs, in terms of number and frequency of diagnostic species. We also established formal definitions for the various alliance concepts based on comparison of the total cover of the diagnostic species groups, and evaluated alternative geographical subdivisions of beech forests. Results The first and second division levels of TWINSPAN followed the temperature and soil pH gradients, while lower divisions were mainly geographical. We grouped the 22 OPUs of Fagus sylvatica forests into acidophytic, meso-basiphytic and thermo-basiphytic beech forests, and separated two OPUs of F. orientalis forests. However, a solution with only two ecologically defined alliances of F. sylvatica forests (acidophytic vs basiphytic) was clearly superior with regard to number and frequency of diagnostic species. In contrast, when comparing groupings with three to six geographical alliances of basiphytic beech forests, respectively, we did not find a strongly superior solution. Conclusions We propose to classify F. sylvatica forests into 15 suballiances - three acidophytic and 12 basiphytic ones. Separating these two groups at alliance or order level was clearly supported by our results. Concerning the grouping of the 12 basiphytic suballiances into ecological or geographical alliances, as advocated by many authors, we failed to find an optimal solution. Therefore, we propose a multi-dimensional classification of basiphytic beech forests, including both ecological and geographical groups as equally valid concepts which may be used alternatively depending on the purpose and context of the classification. [ABSTRACT FROM AUTHOR]

In recent years, interest in availability of georeferenced vegetation plots has stimulated or reinvigorated national initiatives to compile these data. In Italy, eleven vegetation databases are currently registered in the Global Index of Vegetation-Plot Databases (GIVD). In this paper we describe the Vegetation Plot Database - Sapienza University of Rome (VPD-Sapienza; GIVD code EU-IT-011), which started in 2012 and rapidly became one of the most prominent ones with a total of 21,917 georeferenced vegetation plots spanning across the country. These plots include 286,650 vascular plant species occurrences. Most of the plots belong to temperate deciduous forests (Querco roboris-Fagetea sylvaticae) and broadleaved evergreen forests (Quercetea ilicis), which together account for about 40% of the database. The need for the establishment of a national federated database integrating other national, regional, local and thematic databases is discussed in order to avoid setbacks such as duplication of data, taxonomic and syntaxonomic inconsistencies, and reduced efficiencies in collaborative projects, both at national and international levels. [ABSTRACT FROM AUTHOR]

Black locust (Robinia pseudoacacia L.) is a fast growing tree species native to temperate North America, and widely diffused and naturalized in Europe. It is one of the candidate species for establishing bioenergy plantations on marginal lands in temperate and sub-Mediterranean regions. This potential is in contrast to its well-known invasive habit, leading to a potential damage to plant biodiversity in many European countries. Advise against black locust plantation in regions where it is already invasive has been issued by several international reports, as well as the adoption of mitigation measures (e.g., "containment" buffer zones) to prevent the spread of the species into natural and semi-natural habitats. In the Mediterranean basin, however, no studies have been carried out aimed at quantifying the escape rate of black locust saplings from plantation stands and its recruitment into natural habitats, together with the effectiveness of a buffer zone in reducing the spread. In this study we investigated the spread of black locust along 35 transects surrounding three 20-year- old plantations and including three different land cover types: abandoned arable land, semi-natural woodland and a buffer zone (orchards) with a low degree of farming input. In addition, the effect of soil disturbance on seed propagation was investigated. Our results demonstrate that the density of black locust regeneration is strongly affected by the land cover, abandoned agricultural land being the most prone to black locust colonization. Contrastingly, the spread was minimal in the buffer zone and negligible in semi-natural woodland. During the investigated year, seed generative propagation was also negligible. The semi-natural woodland seems to resist well to black locust invasion, though further observations are needed to assess the consequences of stand harvesting disturbance as well, according to local standard forest management. Buffer zones seem to be very effective in controlling black locust invasion. Best management practices, with active farming inputs, are also discussed. [ABSTRACT FROM AUTHOR]

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 vegetationplot 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 Syn-BioSys 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. [ABSTRACT FROM AUTHOR]

Rendiconti Lincei has dedicated a Special Issue to present a number of case studies highlighting the role played by the European Vegetation Survey Working Group established by Sandro Pignatti and his colleagues in support to the implementation of the Habitats Directive. Studies include theoretical analyses as well as researches conducted at different spatial and temporal scales focused on the assessment and monitoring of the conservation status of habitats and target plant species, identification of new habitats and subtypes, and their diagnostic species. [ABSTRACT FROM AUTHOR]

Aims Classification of vegetation is an essential tool to describe, understand, predict and manage biodiversity. Given the multiplicity of approaches to classify vegetation, it is important to develop international consensus around a set of general guidelines and purpose-specific standard protocols. Before these goals can be achieved, however, it is necessary to identify and understand the different choices that are made during the process of classifying vegetation. This paper presents a framework to facilitate comparisons between broad-scale plot-based classification approaches. Results Our framework is based on the distinction of four structural elements (plot record, vegetation type, consistent classification section and classification system) and two procedural elements (classification protocol and classification approach). For each element we describe essential properties that can be used for comparisons. We also review alternative choices regarding critical decisions of classification approaches; with a special focus on the procedures used to define vegetation types from plot records. We illustrate our comparative framework by applying it to different broad-scale classification approaches. Conclusions Our framework will be useful for understanding and comparing plot-based vegetation classification approaches, as well as for integrating classification systems and their sections. [ABSTRACT FROM AUTHOR]

The study of vegetation phenology has great relevance in many fields since the importance of knowing timing and shifts in periodic plant life cycle events to face the consequences of global changes in issues such as crop production, forest management, ecosystem disturbances, and human health. The availability of high spatial resolution and dense revisit time satellite observations, such as Sentinel-2 satellites, allows high resolution phenological metrics to be estimated, able to provide key information from time series and to discriminate vegetation typologies. This paper presents an automated and transferable procedure that combines validated methodologies based on local curve fitting and local derivatives to exploit full satellite Earth observation time series to produce information about plant phenology. Multivariate statistical analysis is performed for the purpose of demonstrating the capacity of the generated smoothed vegetation curve, temporal statistics, and phenological metrics to serve as temporal discriminants to detect forest ecosystems processes responses to environmental gradients. The results show smoothed vegetation curve and temporal statistics able to highlight seasonal gradient and leaf type characteristics to discriminate forest types, with additional information about forest and leaf productivity provided by temporal statistics analysis. Furthermore, temporal, altitudinal, and latitudinal gradients are obtained from phenological metrics analysis, which also allows to associate temporal gradient with specific phenophases that support forest types distinction. This study highlights the importance of integrated data and methodologies to support the processes of vegetation recognition and monitoring activities. [ABSTRACT FROM AUTHOR]

In the light of the "Biological Diversity" concept, habitats are cardinal pieces for biodiversity quantitative estimation at a local and global scale. In Europe EUNIS (European Nature Information System) is a system tool for habitat identification and assessment. Earth Observation (EO) data, which are acquired by satellite sensors, offer new opportunities for environmental sciences and they are revolutionizing the methodologies applied. These are providing unprecedented insights for habitat monitoring and for evaluating the Sustainable Development Goals (SDGs) indicators. This paper shows the results of a novel approach for a spatially explicit habitat mapping in Italy at a national scale, using a supervised machine learning model (SMLM), through the combination of vegetation plot database (as response variable), and both spectral and environmental predictors. The procedure integrates forest habitat data in Italy from the European Vegetation Archive (EVA), with Sentinel-2 imagery processing (vegetation indices time series, spectral indices, and single bands spectral signals) and environmental data variables (i.e., climatic and topographic), to parameterize a Random Forests (RF) classifier. The obtained results classify 24 forest habitats according to the EUNIS III level: 12 broadleaved deciduous (T1), 4 broadleaved evergreen (T2) and eight needleleaved forest habitats (T3), and achieved an overall accuracy of 87% at the EUNIS II level classes (T1, T2, T3), and an overall accuracy of 76.14% at the EUNIS III level. The highest overall accuracy value was obtained for the broadleaved evergreen forest equal to 91%, followed by 76% and 68% for needleleaved and broadleaved deciduous habitat forests, respectively. The results of the proposed methodology open the way to increase the EUNIS habitat categories to be mapped together with their geographical extent, and to test different semi-supervised machine learning algorithms and ensemble modelling methods. [ABSTRACT FROM AUTHOR]