regions; 1970-2015 time period), we analysed the species pool and frequency of alien vascular plants with respect to geographic origin and life-forms, and the levels of invasion across the European Nature Information System (EUNIS) woodland habitats. Results:We found a total of 386 alien plant species (comprising 7% of all recorded vascular plants). Aliens originating from outside of and from within Europe were almost equally represented in the species pool (192 vs. 181 species) but relative frequency was skewed towards the former group (77% vs. 22%) due, to some extent, to the frequent occurrence of Impatiens parviflora (21% frequency among alien plants).Phanerophytes were the most species-rich life-form (148 species) and had the highest representation in terms of relative frequency (39%) among aliens in the dataset. Apart from Europe (181 species), North America was the most important source of alien plants (109 species). At the local scale, temperate and boreal softwood riparian woodland (5%) and mire and mountain coniferous woodland (<1%) had the highest and lowest mean relative alien species richness (percentage of alien species per plot), respectively. Main conclusions:Our results indicate that European woodlands are prone to alien plant invasions especially when exposed to disturbance, fragmentation, alien propagule pressure and high soil nutrient levels. Given the persistence of these factors in the landscape, competitive alien plant species with a broad niche, including alien trees and shrubs, are likely to persist and spread further into European woodlands. K E Y W O R D SEUNIS, exotic, forest, invasive plants, life-form, neophyte, non-native, origin, tree | INTRODUCTIONGlobalization has triggered a massive spread of plant species to areas outside their native distribution ranges (van Kleunen et al., 2015).Some alien species persist only temporarily as casuals in the new area, while others can overcome local abiotic and reproductive barriers to establish self-sustaining populations (Richardson et al., 2000). Some naturalized aliens become invasive, that is they can spread in large numbers and across considerable distances (Richardson et al., 2000) or can have detrimental environmental and socio-economic impacts Woodlands cover a third of Europe's terrestrial area (Forest Europe, 2015; note that we use "woodland" as a synonym of "forest" in our article). In the past, they were logged and transformed to cropland andother open landscape types on a massive scale (Behre, 1988). Today, most European woodlands are composed of stands where the mean tree age is only 60 years (Vilén et al., 2012). Woodlands-and stands with old trees in particular-are generally thought to be resistant to alien plant invasions given the specific abiotic conditions in their herb layer, such as a dense canopy cover and a thick litter layer (Rejmánek, 2015). However, an increasing number of studies has questioned this assumption (e.g., Essl, Mang, & Moser, 2012;Kohli, Jose, Pal Singh, & Batish, 2009;Martin, Canham, & Marks, 2009;Re...

Understanding drivers of success for alien species can inform on potential future invasions. Recent conceptual advances highlight that species may achieve invasiveness via performance along at least three distinct dimensions: 1) local abundance, 2) geographic range size, and 3) habitat breadth in naturalized distributions. Associations among these dimensions and the factors that determine success in each have yet to be assessed at large geographic scales. Here, we combine data from over one million vegetation plots covering the extent of Europe and its habitat diversity with databases on species’ distributions, traits, and historical origins to provide a comprehensive assessment of invasiveness dimensions for the European alien seed plant flora. Invasiveness dimensions are linked in alien distributions, leading to a continuum from overall poor invaders to super invaders—abundant, widespread aliens that invade diverse habitats. This pattern echoes relationships among analogous dimensions measured for native European species. Success along invasiveness dimensions was associated with details of alien species’ introduction histories: earlier introduction dates were positively associated with all three dimensions, and consistent with theory-based expectations, species originating from other continents, particularly acquisitive growth strategists, were among the most successful invaders in Europe. Despite general correlations among invasiveness dimensions, we identified habitats and traits associated with atypical patterns of success in only one or two dimensions—for example, the role of disturbed habitats in facilitating widespread specialists. We conclude that considering invasiveness within a multidimensional framework can provide insights into invasion processes while also informing general understanding of the dynamics of species distributions.

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In this paper we introduce mathematical model and real-time numerical method for segmentation of Natura 2000 habitats in satellite images by evolving open planar curves. These curves in the Lagrangian formulation are driven by a suitable velocity vector field, projected to the curve normal. Besides the vector field, the evolving curve is influenced also by the local curvature representing a smoothing term. The model is numerically solved using the flowing finite volume method discretizing the arising intrinsic partial differential equation with Dirichlet boundary conditions. The time discretization is chosen as an explicit due to the ability of real-time edge tracking. We present the results of semi-automatic segmentation of various areas across Slovakia, from the riparian forests to mountainous areas with scrub pine. The numerical results were compared to habitat boundaries tracked by GPS device in the field by using the mean and maximal Hausdorff distances as criterion.

In this paper, we present a mathematical model and numerical method designed for the segmentation of satellite images, namely to obtain in an automated way borders of Natura 2000 habitats from Sentinel-2 optical data. The segmentation model is based on the evolving closed plane curve approach in the Lagrangian formulation including the efficient treatment of topological changes. The model contains the term expanding the curve in its outer normal direction up to the region of habitat boundary edges, the term attracting the curve accurately to the edges and the smoothing term given by the influence of local curvature. For the numerical solution, we use the flowing finite volume method discretizing the arising advection-diffusion intrinsic partial differential equation including the asymptotically uniform tangential redistribution of curve grid points. We present segmentation results for satellite data from a selected area of Western Slovakia (Záhorie) where the so-called riparian forests represent the important European Natura 2000 habitat. The automatic segmentation results are compared with the semi-automatic segmentation performed by the botany expert and with the GPS tracks obtained in the field. The comparisons show the ability of our numerical model to segment the habitat areas with the accuracy comparable to the pixel resolution of the Sentinel-2 optical data.

The NaturaSat software integrates various image processing techniques together with vegetation data, into one multipurpose tool that is designed for performing facilities for all requirements of habitat exploration, all in one place. It provides direct access to multispectral Sentinel-2 data provided by the European Space Agency. It supports using these data with various vegetation databases, in a user-friendly environment, for, e.g., vegetation scientists, fieldwork experts, and nature conservationists. The presented study introduces the NaturaSat software, describes new powerful tools, such as the semi-automatic and automatic segmentation methods, and natural numerical networks, together with validated examples comparing field surveys and software outputs. The software is robust enough for field work researchers and stakeholders to accurately extract target units’ borders, even on the habitat level. The deep learning algorithm, developed for habitat classification within the NaturaSat software, can also be used in various research tasks or in nature conservation practices, such as identifying ecosystem services and conservation value. The exact maps of the habitats obtained within the project can improve many further vegetation and landscape ecology studies.

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