Description: Kelp forests represent a major habitat type in coastal waters worldwide and their structure and distribution is predicted to change due to global warming. Despite their ecological and economical importance, there is still a lack of reliable spatial information on their abundance and distribution. In recent years, various hydroacoustic mapping techniques for sublittoral environments evolved. However, in turbid coastal waters, such as off the island of Helgoland (Germany, North Sea), the kelp vegetation is present in shallow water depths normally excluded from hydroacoustic surveys. In this study, single beam survey data consisting of the two seafloor parameters roughness and hardness were obtained with RoxAnn from water depth between 2 and 18 m. Our primary aim was to reliably detect the kelp forest habitat with different densities and distinguish it from other vegetated zones. Five habitat classes were identified using underwater-video and were applied for classification of acoustic signatures. Subsequently, spatial prediction maps were produced via two classification approaches: Linear discriminant analysis (LDA) and manual classification routine (MC). LDA was able to distinguish dense kelp forest from other habitats (i.e. mixed seaweed vegetation, sand, and barren bedrock), but no variances in kelp density. In contrast, MC also provided information on medium dense kelp distribution which is characterized by intermediate roughness and hardness values evoked by reduced kelp abundances. The prediction maps reach accordance levels of 62% (LDA) and 68% (MC). The presence of vegetation (kelp and mixed seaweed vegetation) was determined with higher prediction abilities of 75% (LDA) and 76% (MC). Since the different habitat classes reveal acoustic signatures that strongly overlap, the manual classification method was more appropriate for separating different kelp forest densities and low-lying vegetation. It became evident that the occurrence of kelp in this area is not simply linked to water depth. Moreover, this study shows that the two seafloor parameters collected with RoxAnn are suitable indicators for the discrimination of different densely vegetated seafloor habitats in shallow environments.

Description: In the paper "Salinity as a tool for strain selection in recirculating land-based production of Ulva spp. from germlings to adults" (Cardoso et al. 2023) the relative growth rates of adult thalli of different Ulva species and strains were measured to evaluate the impact of different salinity treatments. We hypothesised that salinity impacts the of the seaweeds and that different strains are adapted to different salinities. With this data we aimed to use salinity as a tool for selecting the strain with the highest and determine the optimal salinity to grow it in the future in a recirculating land-based system. Four strains were tested (U. lacinulata and U. linza from the NE-Atlantic and U. lacinulata and U. flexuosa from the Mediterranean). The NE- Atlantic strains were collected in the Óbidos Lagoon, Portugal in January 2021 and were cultivated in laboratory conditions in the Alfred Wegener Institute (AWI), Bremerhaven, Germany. The Mediterranean strains belong to an AWI collection and were isolated in 1986 and 1987 (U. flexuosa and U. lacinulata, respectively). The cultivation of the Mediterranean species with the purpose of using them for experiments started in June 2021. To recreate the conditions of a large-scale system, the strains were grown previously and during the experiment in a medium of artificial seawater (Seequasal-Salz, Seequasal Salz Production and Trade GmbH, Münster, Germany) enriched with a cheaper commercial fertilizer Blaukorn (COMPO SANA®, Germany). A uniform amount of fresh thalli from each species and population were placed into 1 L glass beakers with salinities of 10, 15, 20 and 30 PSU (each condition n = 3) and cultivated over 3 weeks. The fresh weight was measured once a week by collecting the seaweed and removing the excess water with absorbent paper three times before weighing the samples (Sartorius, Germany). Every time, each sample was measured 3 times. Based on the fresh weight measured each week, it was possible to calculate the of each strain throughout the experiment.