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Aim Ideally, datasets for species distribution modelling ( SDM) contain evenly sampled records covering the entire distribution of the species, confirmed absences and auxiliary ecophysiological data allowing informed decisions on relevant predictors. Unfortunately, these criteria are rarely met for marine organisms for which distributions are too often only scantly characterized and absences generally not recorded. Here, we investigate predictor relevance as a function of modelling algorithms and settings for a global dataset of marine species. Location Global marine. Methods We selected well-studied and identifiable species from all major marine taxonomic groups. Distribution records were compiled from public sources (e.g., OBIS, GBIF, Reef Life Survey) and linked to environmental data from Bio- ORACLE and MARSPEC. Using this dataset, predictor relevance was analysed under different variations of modelling algorithms, numbers of predictor variables, cross-validation strategies, sampling bias mitigation methods, evaluation methods and ranking methods. SDMs for all combinations of predictors from eight correlation groups were fitted and ranked, from which the top five predictors were selected as the most relevant. Results We collected two million distribution records from 514 species across 18 phyla. Mean sea surface temperature and calcite are, respectively, the most relevant and irrelevant predictors. A less clear pattern was derived from the other predictors. The biggest differences in predictor relevance were induced by varying the number of predictors, the modelling algorithm and the sample selection bias correction. The distribution data and associated environmental data are made available through the R package marinespeed and at . Main conclusions While temperature is a relevant predictor of global marine species distributions, considerable variation in predictor relevance is linked to the SDM set-up. We promote the usage of a standardized benchmark dataset (Marine SPEED) for methodological SDM studies. [ABSTRACT FROM AUTHOR]

Data providers in the Ocean Biogeographic Information System (OBIS) network are not just recording species occurrences, they are also recording sampling methodology details and measuring environmental and biotic variables. In order to make OBIS an effective data sharing platform, it needs to be able to store and exchange these data in such a way that they can easily be interpreted by end users, as well as by the tools which will be created to search, analyze and visualize the integrated data. OBIS makes use of Darwin Core Archives (DwC-A) for exchanging data between data providers, regional and thematic nodes, and the central OBIS database. However, due to limitations of the DwC-A schema, this data format is currently not suitable for storing sampling event details or sample related measurements, as well as biotic measurements. In order to overcome this problem, OBIS has created a new extension type based on the existing MeasurementOrFacts extension (De Pooter et al. 2017). This ExtendedMeasurementOrFacts extension adds an occurrenceID field, which allows linking biotic measurements to occurrences, even if the archive contains an event table and sample level measurements or facts. In addition, identifiers for measurement types, values and units can now be added in the new measurementTypeID, measurementValueID and measurementUnitID fields. These identifiers link to vocabularies such as the BODC NERC Vocabulary, and greatly improve the interoperability and reusability of the OBIS datasets. [ABSTRACT FROM AUTHOR]

A correction to the article "Analysing the Content of the European Ocean Biogeographic Information System (EurOBIS): Available Data, Limitations, Prospects and a Look at the Future" that was published online on April 27, 2011 is presented.