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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.

Anthropogenic modifications of sediment load can cause ecological degradation in stream and river ecosystems. However, in practice, identifying when and where sediment is the primary cause of ecological degradation is a challenging task. Biological communities undergo natural cycles and variation over time, and respond to a range of physical, chemical and biological pressures. Furthermore, fine sediments are commonly associated with numerous other pressures that are likely to influence aquatic biota. The use of conventional, non-biological monitoring to attribute cause and effect would necessitate measurement of multiple parameters, at sufficient temporal resolution, and for a significant period of time. Biomonitoring tools, which use low-frequency measurements of biota to gauge and track changes in the environment, can provide a valuable alternative means to detecting the effects of a given pressure. In this study, we develop and test an improved macroinvertebrate, family-level and mixed-level biomonitoring tool for fine sediment. Biologically-based classifications of sediment sensitivity were supplemented by using empirical data of macroinvertebrate abundance and percentage fine sediment, collected across a wide range of temperate river and stream ecosystems (model training datasetn=2252) to assign detailed individual sensitivity weights to taxa. An optimum set of weights were identified by non-linear optimisation, as those that resulted in the highest Spearman’s rank correlation coefficient between the index (called the Empirically-weighted Proportion of Sediment-sensitive Invertebrates index; E-PSI) scores and deposited fine sediment in the model training dataset. The family and mixed-level tools performed similarly, with correlations with percentage fine sediment in the test dataset (n=84) ofrs=−0.72 andrs=−0.70p<0.01. Testing of the best performing family level version, over agriculturally impacted sites (n=754) showed similar correlations to fine sediment (rs=−0.68p<0.01). The tools developed in this study have retained their biological basis, are easily integrated into contemporary monitoring agency protocols and can be applied retrospectively to historic datasets. Given the challenges of non-biological conventional monitoring of fine sediments and determining the biological relevance of the resulting data, a sediment-specific biomonitoring approach is highly desirable and will be a useful addition to the suite of pressure-specific biomonitoring tools currently used to infer the causes of ecological degradation

Boreal Baltic coastal wetlands differ markedly from temperate salt marshes by their generally low maximum elevation (between 0 and 1 m above m.s.l.), low seaward gradients and the irregular nature of flooding that is characteristic of the NE Baltic Sea coastal region. As a result of these factors these wetlands have been considered to be threatened by future sea level rise. This study presents results for two Boreal Baltic coastal wetland sites in Estonia using The recent acceleration in the rate of global sea-level rise may subtly alter this relationship. However current rates of GIA and sedimentation will continue to maintain the progradation of Boreal Baltic coastal wetlands in the coming decades.

Question: What are the effects of grazing abandonment on the vegetation composition of Estonian coastal wetlands? Location: Vormsi Island and Silma Nature Reserve in western Estonia, Europe. Methods: Local knowledge and field reconnaissance were used to identify current and historical management levels of wetland sites within the west Estonian study area. Nine study sites, with varying management histories, were selected comprising an area of 287 ha. A total of 198 quadrats were taken from 43 distinct vegetation patches in five of the sites. TWINSPAN analysis was used to identify community type, and a phytosociological key was constructed for character taxa. This vegetation classification was then applied within a GIS-based context to classify all the study sites, using a ground survey technique and 1:2000 scale air photos. Results: We identified 11 different brackish coastal wetland community types. Indicator species were defined with community characteristics for the seven main vegetation types readily recognisable in the field. Coastal wet grasslands were most extensive in grazed sites, or sites that had been more intensively grazed, while abandoned sites were largely composed of Phragmites australis stands, tall grassland, and scrub. Site variations based on vegetation composition were significantly correlated with past grazing intensity. Plant community types showed significant edaphic differences, with particularly low soil moisture and high conductivity and pH for open pioneer patches compared to other vegetation types. Conclusion: Abandonment of traditionally grazed coastal grasslands threatens their characteristic biodiversity. This study found that grazing abandonment reduced the extent of coastal wetland grasslands of particular conservation value. Nevertheless, plant species of conservation interest were found across the sequence of community types described. The study shows that grazing is an important factor influencing coastal wetland plant communities but suggests that vegetation distribution is affected by environmental variables, such as topography.