This study presents a quantitative approach to mapping benthophagous fish feeding grounds. This approach combines the spatial biomass distribution of benthic prey items and their importance for the diets of predators. A point based biomass data of macrozoobenthos together with a set of environmental factors was used to develop Random Forests models that produce continuous biomass distribution layers for individual prey species. Depending on the diet composition and the importance of prey for fish feeding, these layers are overlaid and an integrated GIS map of the seabed showing the quality of feeding grounds is generated. These maps provide a useful basis for conservation and marine spatial planning. In addition, this method could be applied to the mapping of resources used by other benthophagous organisms. The method is presented using the example of three common Baltic fish species: cod, flounder and viviparous eelpout

SummaryThe killer shrimp Dikerogammarus villosus was recorded for the first time in Lithuanian waters in 2015. The species was detected in three sites in the Curonian Lagoon (on two buoys in the lagoon strait and the harbour, and one littoral sampling site) and in the mouth of the Šventoji River. The species presence in the buoy fouling suggests the involvement of shipping in species introduction. Most likely D. villosus has arrived to the Curonian Lagoon with commercial ships, while the invasion into the mouth of the Šventoji River may be associated with leisure shipping as the port situated therein is not currently functioning. Further northward expansion of the killer shrimp in the Baltic Sea basin seems very probable. As the species is highly aggressive, alterations of local macroinvertebrate assemblages can also be predicted

Underwater imagery is widely used for a variety of applications in marine biology and environmental sciences, such as classification and mapping of seabed habitats, marine environment monitoring and impact assessment, biogeographic reconstructions in the context of climate change, etc. This approach is relatively simple and cost-effective, allowing the rapid collection of large amounts of data. However, due to the laborious and time-consuming manual analysis procedure, only a small part of the information stored in the archives of underwater images is retrieved. Emerging novel deep learning methods open up the opportunity for more effective, accurate and rapid analysis of seabed images than ever before.

Over the last few decades, climate change in Svalbard (European Arctic) has led to the emergence and growth of periglacial coastal lagoons in the place of retreating glaciers. In these emerging water bodies, new ecosystems are formed, consisting of elements presumably entering the lagoon from the melting glacier, the surrounding tundra water bodies and the coastal ocean. The data presented here were collected from an emerging lagoon in the western region of Spitsbergen, Svalbard, situated between the retreating Eidembreen Glacier and Eidembukta Bay in 2022–2023. The current size of the lagoon area is approximately 6 square kilometers. The sampling was carried out at 26 sites across various sections of the lagoon, spanning from close proximity to the glacier to the furthest point away. The dataset contains the results of bacterioplankton (total cell concentration and carbon biomass), phytoplankton (taxonomic composition, cell size for selected taxa, abundance, biomass and carbon biomass), zooplankton (taxonomic composition, abundance), and environmental DNA (eDNA) metabarcoding. The dataset will be utilized to provide a comprehensive description of the structure of the lagoon ecosystem. It will also facilitate a comparison of its various parts, which vary in terms of their age of origin, i.e., release from the glacier. Additionally, the dataset will aid in the understanding of the intricate interactions between the freshwater and marine elements of the ecosystem. It can be used for comparative analysis of biodiversity assessment using eDNA and traditional microscopy methods in the identification of phyto- and zooplankton. Furthermore, these data can be utilized for environmental monitoring, tracing the temporal shifts and conducting comparative analysis of periglacial lagoons that are emerging in various regions of Svalbard as a result of climate change.