Highlights:-Plant communities in coastal wetlands are at risk due to the impacts of global change-Knowing the distribution of plant communities is essential for nature conservation-Communities distribution maps were produced using a UAV-based multispectral sensor-The Random Forest classifier yielded the highest classification accuracy-Species diversity and aboveground biomass affect the classification performance ABSTRACT Coastal meadows worldwide are subjected to habitat degradation due to abandonment, intensification and the impacts of global change. In order to protect and restore these habitats and ensure the supply of valuable ecosystem services, it is necessary to know the extent and location of plant communities in coastal meadows. In this study, five plant communities were mapped at very high resolution in three different study sites in West Estonia. A fixed wing UAV was used to obtain multispectral images and derive a set of vegetation indices. Two different image classification techniques were used to cluster the vegetation indices maps and produce plant community distribution maps. The highest classification accuracy was obtained using a Random Forest classifier and 13 vegetation indices. Additionally, the spectral characteristics of the training samples were correlated with aboveground biomass and species diversity. Both biomass and species diversity were positively correlated with the spectral diversity of training samples and are thus likely to have an effect on the classification accuracy. The results of this study highlight the need to utilize a wide array of vegetation indices and assess the spectral characteristics of training samples in order to obtain high classification accuracies and understand the nature of misclassification errors. The resulting maps provide a solid foundation for global change impact assessment and habitat management and restoration in coastal meadows.

The red list has become a ubiquitous tool in the conservation of species. We analyzed contemporary trends in the threat levels of European orchids, in total 166 species characterized in 27 national red lists, in relation to their reproductive biology and growth form, distribution area, and land cover where they occur. We found that species in central Europe are more threatened than those in the northern, southern, or Atlantic parts of Europe, while species were least threatened in southern Europe. Nectarless and tuberous species are significantly more threatened than nectariferous and rhizomatous taxa. Land cover (ratios of artificial land cover, area of pastures and grasslands, forests and inland wetlands) also significantly impacted the threat level. A bigger share of artificial land cover increases threat, and a bigger share of pasture and grassland lowers it. Unexpectedly, a bigger share of inland wetland area in a country increased threat level, which we believe may be due to the threatened nature of wetlands themselves relative to other natural land cover types. Finally, species occurring in multiple countries are on average less threatened. We believe that large‐scale analysis of current IUCN national red lists as based on their specific categories and criteria may particularly inform the development of coordinated regional or larger‐scale management strategies. In this case, we advocate for a coordinated EU protection and restoration strategy particularly aimed at central European orchids and those occurring in wetland area.

The European Union (EU) Horizon 2020 Coordination and Support Action ESMERALDA aimed at developing guidance and a flexible methodology for Mapping and Assessment of Ecosystems and their Services (MAES) to support the EU member states in the implementation of the EU Biodiversity Strategy’s Target 2 Action 5. ESMERALDA’s key tasks included network creation, stakeholder engagement, enhancing ecosystem services mapping and assessment methods across various spatial scales and value domains, work in case studies and support of EU member states in MAES implementation. Thus ESMERALDA aimed at integrating various project outcomes around four major strands: i) Networking, ii) Policy, iii) Research and iv) Application. The objective was to provide guidance for integrated ecosystem service mapping and assessment that can be used for sustainable decision-making in policy, business, society, practice and science at EU, national and regional levels. This article presents the overall ESMERALDA approach of integrating the above-mentioned project components and outcomes and provides an overview of how the enhanced methods were applied and how they can be used to support MAES implementation in the EU member states. Experiences with implementing such a large pan-European Coordination and Support Action in the context of EU policy are discussed and recommendations for future actions are given.

Estonia and potentially other regions facing similar challenges. MATERIALS AND METHODS Study areaEstonia is located in the Baltic region between Latvia, Russia and Finland, in the border between the Boreal and Nemoral zones (Metzger et al., 2005). Despite its relatively small size (45228 km 2 ) Estonia exhibits a high geological, morphological, and climatic diversity (Arold, 2005). Within Estonia, there are ten semi-natural grassland habitats, based on the Annex I Habitats Directive classification (Council Directive, 1992). Semi-natural grasslands in Estonia have exceptionally high levels of biodiversity, in particular wooded meadows (76 species/m 2 ; Kukk 2004, 1997), alvars (63 species/m 2 ; Partel et al., 1999), floodplain meadows (50 species/m 2 ; Truus and Puusild, 2009), and coastal meadows (34 species/m 2 ; Burnside et al., 2007), among the most species rich habitats in Northern Europe (Benstead et al., 1999). Semi-natural grasslands are the result of long term, low-intensity management practices, in the form of grazing and mowing (Paal, 1998). In Estonia, some of these semi-natural habitats have been managed for centuries (Helm et al., 2005), leading to iconic landscapes such as the Laelatu wooded meadow (Sammul et al., 2003). The area of seminatural grasslands in Estonia has decreased since the late 1950s (Kana et al., 2008).

Environmental stratifications provide the framework for efficient surveillance and monitoring of biodiversity and ecological resources, as well as modelling exercises. An obstacle for agricultural landscape monitoring in Estonia has been the lack of a framework for the objective selection of monitoring sites. This paper describes the construction and testing of the Environmental Stratification of Estonia (ESE). Principal components analysis (PCA) was used to select the variables that capture the most amount of variation. Seven climate variables and topography were selected and subsequently subjected to the ISODATA clustering routine in order to produce relatively homogeneous environmental strata. The ESE contains eight strata, which have been described in terms of soil, land cover and climatic parameters. In order to assess the reliability of the stratification procedure for the selection of monitoring sites, the ESE was compared with the previous map of Landscape Regions of Estonia and correlated with five environmental datasets. All correlations were significant. The stratification has therefore already been used to extend the current series of samples in agricultural landscapes into a more statistically robust series of monitoring sites. The potential for applying climate change scenarios to assess the shifts in the strata and associated ecological impacts is also examined.

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Context The Eurasian crane (Grus grus) is an iconic and sensitive species. It is therefore necessary to understand its landscape ecology in order to determine threats. Objectives (1) To map the distribution of cranes and then model their habitat requirements in Estonia, linked to the current level of protection. (2) To determine the environmental characteristics of, and the habitats present in, sites utilized by the birds, and their sensitivity to change.Methods (1) The distribution of cranes was recorded by observation and by tracking individuals. A model of potential breeding sites was compared with the occurrence of the bird in Estonia and then linked to protected sites. (2) The seasonal distribution of the bird was overlaid with a European environmental classification and the CORINE land cover map. A model of climate change was also utilized. Results (1) A new map of European migration routes, wintering and stopover sites is presented. (2)The bird requires a habitat network, with wetlands being essential for nesting and roosting. The composition of habitats used for feeding varies according to geographical location. (3) In Estonia not all potential breeding sites are occupied and many existing sites are not protected. (4) Climate change could threaten populations in the south but could be beneficial in Estonia.Conclusions (1) The existing ecological network in Estonia is adequate to maintain a viable breeding population of the Eurasian crane. (2) Climate change could support the breeding of cranes but complicate their migration and wintering.

Throughout the second half of the 20th Century, the area of semi-natural grasslands in the Baltic States decreased substantially, due to agricultural abandonment in some areas and intensification in more productive soil types. In order to halt the loss of biodiversity and ecosystem services provided by grasslands, the LIFE+ programme funded project, LIFE Viva Grass, aims at developing an integrated planning tool that will support ecosystem-based planning and sustainable grassland management. LIFE Viva Grass integrated planning tool is spatially explicit and allows the user to assess the provision and trade-offs of grassland ecosystem services within eight project case study areas in Estonia, Latvia and Lithuania. In order to ensure methodological adaptability, the structure of the LIFE Viva Grass integrated planning tool follows the framework of the tiered approach. In a multi-tier system, each consecutive tier entails an increase in data requirements, methodological complexity or both. The present paper outlines the adaptation of the tiered approach for mapping and assessing ecosystem services provided by grasslands in the Baltic States. The first tier corresponds to a deliberative decision process: The matrix approach is used to assess the potential supply of grassland ecosystem services based on expert estimations. Expert values are subsequently transferred to grassland units and therefore made spatially explicit. The data collected in the first tier was further enhanced through a Principal Components Analysis (PCA) in order to explore ES bundles in tier 2. In the third tier, Multi-Criteria Decision Analysis is used to target specific policy questions.