Description: Marine benthic ecosystems are difficult to monitor and assess, which is in contrast to modern ecosystem-based management requiring detailed information at all important ecological and anthropogenic impact levels. Ecosystem management needs to ensure a sustainable exploitation of marine resources as well as the protection of sensitive habitats, taking account of potential multiple-use conflicts and impacts over large spatial scales. The urgent need for large-scale spatial data on benthic species and communities resulted in an increasing application of distribution modelling (DM). The use of DM techniques enables to employ full spatial coverage data of environmental variables to predict benthic spatial distribution patterns. Especially, statistical DMs have opened new possibilities for ecosystem management applications, since they are straightforward and the outputs are easy to interpret and communicate. Mechanistic modelling techniques, targeting the fundamental niche of species, and Bayesian belief networks are the most promising to further improve DM performance in the marine realm. There are many actual and potential management applications ofDMsin the marine benthic environment, these are (i) earlywarning systems for species invasion and pest control, (ii) to assess distribution probabilities of species to be protected, (iii) uses in monitoring design and spatial management frameworks (e.g. MPA designations), and (iv) establishing long-term ecosystem management measures (accounting for future climate-driven changes in the ecosystem). It is important to acknowledge also the limitations associated with DM applications in a marine management context as well as considering new areas for futureDMdevelopments. The knowledge of explanatory variables, for example, setting the basis for DM, will continue to be further developed: this includes both the abiotic (natural and anthropogenic) and the more pressing biotic (e.g. species interactions) aspects of the ecosystem.

Description: As the EU’s commitment to renewable energy is projected to grow to 20% of energy generation by 2020, the use of marine renewable energy from wind, wave and tidal resources is increasing. This literature review (233 studies) (i) summarizes knowledge on how marine renewable energy devices affect benthic environments, (ii) explains how these effects could alter ecosystem processes that support major ecosystem services and (iii) provides an approach to determine urgent research needs. Conceptual diagrams were set up to structure hypothesized cause-effect relationships (i.e. paths). Paths were scored for (i) temporal and spatial scale of the effect, (ii) benthic sensitivity to these effects,(iii) the effect consistency and iv) scoring confidence, and consecutively ranked. This approach identified prominent knowledge gaps and research needs about (a) hydrodynamic changes possibly resulting in altered primary production with potential consequences for filter feeders, (b) the introduction and range expansion of non-native species (through stepping stone effects) and, (c) noise and vibration effects on benthic organisms. Our results further provide evidence that benthic sensitivity to offshore renewable effects is higher than previously indicated. Knowledge on changes of ecological functioning through cascading effects is limited and requires distinct hypothesis-driven research combined with integrative ecological modelling.

Description: Profound environmental changes, such as drastic sea-ice decline, leave large-scale ecological footprints on the distribution and composition of marine biota in the Arctic. Currently, the impact of such stressors is not sufficiently understood due to the lack of pan-Arctic data that allow for estimating ecological baselines as well as modelling current and forecast potential changes in benthic biodiversity and ecosystem functioning. Here, we introduce the PAN-Arctic data collection of benthic BIOtas (PANABIO) and discuss its timeliness, potential, and details of its further development. The data collection contains individual datasets with records (presence, counts, abundance, or biomass) of benthic fauna, usually at genus level or species level, which were identified in field samples obtained at point-referenced locations (stations) by means of grabs, towed gear, or seabed imaging. The data cover the entire pan-Arctic realm, i.e.The central Arctic Ocean, Chukchi Sea, East Siberian Sea, Laptev Sea, Kara Sea, Barents Sea (including the White Sea), Svalbard waters, Greenland Sea, Norwegian Sea, Canadian Archipelago, Beaufort Sea, and Bering Sea as well as some adjacent sub-Arctic regions (Sea of Japan, Gulf of Okhotsk). Currently (as of 14 December 2023), PANABIO includes 27 datasets with a total of 126ĝ€¯388 records of 2978 taxa collected from 11ĝ€¯555 samples taken at 10ĝ€¯596 stations during 1095 cruises between 1800 and 2014. These numbers will increase with more data becoming available over time through contributions from PANABIO users. The data collection is available in a PostgreSQL-based data warehouse that can be accessed and queried through an open-Access front-end web service at https://critterbase.awi.de/panabio (last access: 27 February 2024). A snapshot of the current data collection and its 27 individual datasets is also available from the data publisher PANGAEA (10.1594/PANGAEA.963640, Piepenburg et al., 2023).