Description: Current antifouling technologies are based on the continuous release of biocides into the water, and consequently discharge into the environment. Major efforts on more environmentally friendly coatings require efficient testing in laboratory assays, followed by field studies. Barnacles are important fouling organisms worldwide, largely increasing the hydrodynamic drag of ships and damaging coatings on underwater surfaces, and thus extensively used as a model in antifouling research, mostly in static, lab-based systems. Reliable flow-through test assays for the screening of biocide-containing antifouling paints, however, are rare. Herein, a flow-through bioassay was developed to screen for diverse low-release biocide paints, evaluating their effects on pre- and post-settlement traits in barnacles. The assay distinguishes between the effects from direct surface contact and bulk-water effects, crucial when developing low-emission antifouling coatings. In conclusion, this new flow-through bioassay adds a new tool for rapid lab-based first-stage screening of candidate compounds and novel antifouling formulations.

Description: Current antifouling (AF) technologies are based on the continuous release of biocides into the water, and consequently discharge into the environment. Major efforts to develop more environmentally friendly coatings require efficient testing in laboratory assays, followed by field studies. Barnacles are important fouling organisms worldwide, increasing hydrodynamic drag on ships and damaging coatings on underwater surfaces, and thus are extensively used as models in AF research, mostly in static, laboratory-based systems. Reliable flow-through test assays for the screening of biocide-containing AF paints, however, are rare. Herein, a flow-through bioassay was developed to screen for diverse low-release biocide paints, and to evaluate their effects on pre- and post-settlement traits in barnacles. The assay distinguishes between the effects from direct surface contact and bulk-water effects, which are crucial when developing low-emission AF coatings. This flow-through bioassay adds a new tool for rapid laboratory-based first-stage screening of candidate compounds and novel AF formulations.

Description: Aim: To develop and test theory based on connectivity to identify optimal networks of marine protected areas (MPAs) that protect multiple species with a range of dispersal strategies. Location: The eastern North Sea in the Atlantic Ocean. Methods: Theory of finding optimal MPA network is based on eigenvalue perturbation theory applied to population connectivity. Previous theory is here extended to the persistence of multiple species by solving a maximization problem with constraints, which identifies an optimal consensus network of MPAs. The theory is applied to two test cases within a 120,000 km2 area in the North Sea where connectivity was estimated with a biophysical model. In a realistic case, the theory is applied to the protection of rocky-reef habitats, where the biophysical model is parameterized with realistic dispersal traits for key species. Theoretical predictions of optimal networks were validated with a simple metapopulation model. Persistence of optimal consensus MPA networks is compared to randomly selected networks as well as to the existing MPA network. Results: Despite few overlapping MPA sites for the optimal networks based on single dispersal strategies, the consensus network for multiple dispersal strategies performed well for 3 of 4 contrasting strategies even without user-defined constraints. In the test with five realistic dispersal strategies, representing a community on threatened rocky reefs, the consensus network performed equally well compared to solutions for single species. Different dispersal strategies were also protected jointly across the MPA network (93% of sites), in contrast to simulations of the existing MPA network (2% of sites). Consensus networks based on connectivity were significantly more efficient compared to existing MPAs. Main conclusions: Our findings suggest that the new theoretic framework can identify a consensus MPA network that protects a whole community containing species with multiple dispersal strategies.

Description: Predictive species distribution models are mostly based on statistical dependence between environmental and distributional data and therefore may fail to account for physiological limits and biological interactions that are fundamental when modelling species distributions under future climate conditions. Here, we developed a state-of-the-art method integrating biological theory with survey and experimental data in a way that allows us to explicitly model both physical tolerance limits of species and inherent natural variability in regional conditions and thereby improve the reliability of species distribution predictions under future climate conditions. By using a macroalga-herbivore association (Fucus vesiculosus - Idotea balthica) as a case study, we illustrated how salinity reduction and temperature increase under future climate conditions may significantly reduce the occurrence and biomass of these important coastal species. Moreover, we showed that the reduction of herbivore occurrence is linked to reduction of their host macroalgae. Spatial predictive modelling and experimental biology have been traditionally seen as separate fields but stronger interlinkages between these disciplines can improve species distribution projections under climate change. Experiments enable qualitative prior knowledge to be defined and identify cause-effect relationships, and thereby better foresee alterations in ecosystem structure and functioning under future climate conditions that are not necessarily seen in projections based on non-causal statistical relationships alone.