MotivationThe BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community‐led open‐source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.Main types of variables includedThe database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record.Spatial location and grainBioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1,000,000,000,000 cm2).Time period and grainBioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year.Major taxa and level of measurementBioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.Software format.csv and .SQL.

The brackish Baltic Sea hosts species of various origins and environmental tolerances. These immigrated to the sea 10,000 to 15,000 years ago or have been introduced to the area over the relatively recent history of the system. The Baltic Sea has only one known endemic species. While information on some abiotic parameters extends back as long as five centuries and first quantitative snapshot data on biota (on exploited fish populations) originate generally from the same time, international coordination of research began in the early twentieth century. Continuous, annual Baltic Sea-wide long-term datasets on several organism groups (plankton, benthos, fish) are generally available since the mid-1950s. Based on a variety of available data sources (published papers, reports, grey literature, unpublished data), the Baltic Sea, incl. Kattegat, hosts altogether at least 6,065 species, including at least 1,700 phytoplankton, 442 phytobenthos, at least 1,199 zooplankton, at least 569 meiozoobenthos, 1,476 macrozoobenthos, at least 380 vertebrate parasites, about 200 fish, 3 seal, and 83 bird species. In general, but not in all organism groups, high sub-regional total species richness is associated with elevated salinity. Although in comparison with fully marine areas the Baltic Sea supports fewer species, several facets of the system's diversity remain underexplored to this day, such as micro-organisms, foraminiferans, meiobenthos and parasites. In the future, climate change and its interactions with multiple anthropogenic forcings are likely to have major impacts on the Baltic biodiversity.

The European Water Framework Directive (WFD; 2000/60/EG) and the European Marine Strategy Framework Directive (MSFD; 2008/56/EC) were umbrella legislations for fresh and marine waters. It is a challenge for the scientific community to translate the principles of these directives into realistic and accurate approaches. Both directives have the same concept, comparing the current state of an area with that which would be expected under minimal or sustainable human use of that area and in case of degradation, intervening to bring it back to the desired good status. However, each directive used specific principles to fill it in. For the WFD, this was executed during the last decade, and many results of it were already published. This process delivered valuable knowledge on which the implementation of the MSFD can be founded.Therefore, the ICES Benthos Ecology Working Group aimed to stress and discuss some issues, with focus on benthic macro-invertebrates, related to the fulfillment of the principles of both directives. This through the description of (1) how the principles are theoretically filled in by both directives

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 of DMs in the marine benthic environment, these are (i) early warning 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 future DM developments. 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. While the response variables on the other hand are often focused on species presence and some work undertaken on species abundances, it is equally important to consider, e.g. biological traits or benthic ecosystem functions in DM applications. Tools such as DMs are suitable to forecast the possible effects of climate change on benthic species distribution patterns and hence could help to steer present-day ecosystem management.

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