Cleaning up seas using blue growth initiatives: Mussel farming for eutrophication control in the Baltic Sea.

Eutrophication is a serious threat to aquatic ecosystems globally with pronounced negative effects in the Baltic and other semi-enclosed estuaries and regional seas, where algal growth associated with excess nutrients causes widespread oxygen free "dead zones" and other threats to sustainability. Decades of policy initiatives to reduce external (land-based and atmospheric) nutrient loads have so far failed to control Baltic Sea eutrophication, which is compounded by significant internal release of legacy phosphorus (P) and biological nitrogen (N) fixation. Farming and harvesting of the native mussel species (Mytilus edulis/trossulus) is a promising internal measure for eutrophication control in the brackish Baltic Sea. Mussels from the more saline outer Baltic had higher N and P content than those from either the inner or central Baltic. Despite their relatively low nutrient content, harvesting farmed mussels from the central Baltic can be a cost-effective complement to land-based measures needed to reach eutrophication status targets and is an important contributor to circularity. Cost effectiveness of nutrient removal is more dependent on farm type than mussel nutrient content, suggesting the need for additional development of farm technology. Furthermore, current regulations are not sufficiently conducive to implementation of internal measures, and may constitute a bottleneck for reaching eutrophication status targets in the Baltic Sea and elsewhere.

Knowledge to decision in dynamic seas: Methods to incorporate non-indigenous species into cumulative impact assessments for maritime spatial planning.

Incorporating ecosystem changes from non-indigenous species (NIS) is an important task of maritime spatial planning. Maritime spatial planning requires a framework that emphasises ecological functioning in a state of dynamic change, including changes to ecosystem services from functions introduced by new NIS. Adaptable modelling toolsets should be developed that can readily incorporate knowledge of new NIS. In the Baltic Sea, recent NIS examples are the North American mud crab Rhithropanopeus harrisii and the Ponto-Caspian round goby Neogobius melanostomus. We performed environmental niche modelling that predicted N. melanostomus will spread across large areas of the Baltic Sea coast while R. harrisii will be limited to regions with high temperature and low salinity conditions. We then performed a meta-analysis on literature showing effects in the Baltic Sea from these NIS and calculated the standardised effect-sizes on relevant ecosystem services. Half the impacts identified for N. melanostomus were considered to increase ecosystem service outcomes, while all R. harrisii impacts caused apparent decreases. Effect coefficients were incorporated into an online impact assessment tool developed by the Estonian Marine Institute. Users with or without science training can use the portal to estimate areas impacted and changes to natural assets (km2) caused by these NIS and cumulative effects from other pressure-types. Impact estimates are based on best available knowledge from manipulative and correlative experiments and thus form a link between science and management. Dynamic modelling techniques informed from varied ecological and methodological perspectives will effectively advise spatial planners about rapid maritime changes and mitigation actions to reduce NIS impacts especially in the focus areas.

Predicting the cover and richness of intertidal macroalgae in remote areas: a case study in the Antarctic Peninsula.

Antarctica is an iconic region for scientific explorations as it is remote and a critical component of the global climate system. Recent climate change causes a dramatic retreat of ice in Antarctica with associated impacts to its coastal ecosystem. These anthropogenic impacts have a potential to increase habitat availability for Antarctic intertidal assemblages. Assessing the extent and ecological consequences of these changes requires us to develop accurate biotic baselines and quantitative predictive tools. In this study, we demonstrated that satellite-based remote sensing, when used jointly with in situ ground-truthing and machine learning algorithms, provides a powerful tool to predict the cover and richness of intertidal macroalgae. The salient finding was that the Sentinel-based remote sensing described a significant proportion of variability in the cover and richness of Antarctic macroalgae. The highest performing models were for macroalgal richness and the cover of green algae as opposed to the model of brown and red algal cover. When expanding the geographical range of the ground-truthing, even involving only a few sample points, it becomes possible to potentially map other Antarctic intertidal macroalgal habitats and monitor their dynamics. This is a significant milestone as logistical constraints are an integral part of the Antarctic expeditions. The method has also a potential in other remote coastal areas where extensive in situ mapping is not feasible.