Explore OBIS

Taxon search

Common name search

Dataset search

Institute search

Country statistics

ABNJ statistics

Marine World Heritage Sites

EBSA statistics


UK joins the OBIS network through the MBA

The Marine Biological Association (MBA) joined the OBIS network as the OBIS node in the UK.

February 20, 2018 - OBISUK OBIS node

African OBIS training course, 12-16 February, 2018 at KMFRI, Mombasa, Kenya

19 scientists and data managers from 8 African countries (Comoros, Congo, Kenya, Madagascar, Mauritius, Namibia, Nigeria and Tanzania) participated in the OBIS training course, 12-16 February 2018, hosted by the OceanTeacher regional training centre at the Kenya Marine and Fisheries Research Institute (KMFRI) in collaboration with the European OBIS node (hosted by the Flanders Marine Institute, VLIZ, Belgium). The training course will help the region in publishing and accessing marine biodiversity data through the OBIS data platform. All the training materials are available on the OceanTeacher website.

February 20, 2018 - Nina WambijiOBIS training Kenya

INVEMAR joins OBIS as the OBIS node in Colombia

The Marine and Coastal Research Institute of Colombia (INVEMAR) is a national institution in charge of marine research, the generation of data and establishing data management systems to share environmental data. INVEMAR was already involved as a data provider to OBIS during the Census of Marine Life project, which ended in 2010. INVEMAR joined IODE as an Associate Data Unit in 2015, and now become the official OBIS node of Colombia. They wish to make available their technical capacity, infrastructure and experience in marine sciences. We are delighted to welcoming INVEMAR to the OBIS network!

January 11, 2018 - Ward AppeltansOBIS nodes Colombia

Fellowships available - Training course in Marine Species Distribution Modelling, 12-16 March 2018, Belgium

The OceanTeacher Global Academy, in collaboration with OBIS, the Federation of European Phycological Societies (FEPS) and the Spanish phycological society (SEF), will organize a week-long training session on Marine Species Distribution Modelling, 12-16 March 2018, in Oostende, Belgium. The call for applications is open until 14 January 2018.

December 12, 2017 - Ward AppeltansOBIS training Belgium

OBIS Nodes Training course, Oostende, Belgium, 27 Nov - 1 Dec 2017

22 OBIS nodes data managers from 17 countries were trained in the application of ratified Darwin Core terms, using the new OBIS-ENV-DATA standard, which combines sampling events and species occurrences with abiotic/biotic measurements as well as sampling facts. In addition, the new OBIS data access and QC tools (based on OBIS R packages and WoRMS/LifeWatch tools) were thaught. The training course was funded through the IOC's OceanTeacher Global Academy and all the training material is available online.

December 11, 2017 - Ward AppeltansOBIS training Belgium

New data loaded, 30 November 2017

On November 30, 230 new datasets, 9,699,997 new records, and 1,869 new marine species were added to OBIS. The current version of the OBIS database now has 58 million occurrences of 117,901 species. The database report with a full dataset overview is available here.

November 30, 2017 - OBISnew data load

More news...

Subscribe to our mailing list

Tweets by OBIS

Use cases

More losers than winners for Southern Ocean marine life in a warmer future.

biodiversity loss species composition Southern Ocean

A study of the marine invertebrates living in the seas around Antarctica reveals there will be more ‘losers’ than ‘winners’ over the next century as the Antarctic seafloor warms. The results are published in the journal Nature Climate Change.

A team at British Antarctic Survey (BAS) examined the potential distribution of over 900 species of shelf-dwelling marine invertebrates under a warming scenario produced by computer models. The authors used the known distributions of 963 benthic species with ≥20 records, from <1,000 m depth, from south of 40 °S. The records came from the SCAR Biogeographic Atlas of the Southern Ocean & OBIS. The climate models used were an ensemble of 19 different models from the CMIP5 database of mean seafloor temperatures for 2099 under the IPCC RCP8.5 scenario (the most extreme of all the scenarios where emissions continue to rise throughout the 21st century).

Southern Ocean seafloor water temperatures are projected to warm by an average of 0.4 °C over this century with some areas possibly increasing by as much as 2°C. The team conclude that, while some species in some areas will benefit, within the current century, warming temperatures alone are unlikely to result in wholesale extinction or invasion affecting Antarctic seafloor life. However, 79% of Antarctica’s endemic species do face a significant reduction in suitable temperature habitat (an average 12% reduction). Their findings highlight the species and regions most likely to respond significantly (negatively and positively) to warming and have important implications for future management of the region.

Reference: Griffiths, Huw J., Andrew JS Meijers, and Thomas J. Bracegirdle. "More losers than winners in a century of future Southern Ocean seafloor warming." https://www.nature.com/articles/nclimate3377.

Counting seals with drones and thermal imagery

species population UAV OBIS data

Marine megafauna populations are challenging to assess, thanks to their cryptic nature and patchy availability to many forms of remote sensing. The Duke University Marine Robotics and Remote Sensing lab (MaRRS) strives to advance marine wildlife assessment methodology by fusing unoccupied aerial vehicles (UAV), advanced sensor packages and computer vision algorithms. This combination promises to improve the efficiency, economy and safety for surveys that are often tedious and dangerous for those that conduct them in remote parts of the world.

In the spring of 2015, the MaRRS lab conducted surveys over two grey seal breeding colonies in Nova Scotia using a small fixed-wing UAV called an “ebee”, taking pictures of the colonies with standard RGB and thermal cameras mounted in the belly of the aircraft. In the thermal images, seal pups and adults showed up as hot “blips” on a frigid background of ice and frozen earth, presenting an ideal opportunity to compare how humans and automated machine learning approaches detect and count animals in remotely-sensed data. The MaRRS lab computer vision algorithm proved extremely accurate, yielding total seal counts only 2% different than manual counts by humans, even tackling a long-time hurdle in automated detection by consistently discriminating seals within closely packed “piles”.

The above case study is widely applicable to species that seasonally aggregate on land, particularly pinnipeds and colonial seabirds. UAVs, by their very nature, are capable of rapid deployment and can take advantage of temporal windows where weather is good and animals are visible on land. The MaRRS computer vision algorithm operates in the common program ArcMap (ESRI), and is designed for quick modification to apply to other pinnipeds and even entirely different genera. This type of flexible and easily-modifiable model design is critical for practical applications in wildlife management. Algorithm development is time consuming and if time must be taken to extensively retrain a model for each new dataset, many advantages in efficiency are lost over traditional, manual-counting methods.

As UAVs proliferate and more data is collected, analysis becomes a bottleneck for getting relevant information to resource managers and decision makers. Combining UAVs with computer vision is a way to stay ahead of the curve and ensure that big data is an advantage and not a stumbling-block for wildlife management.

In total, 3,355 grey seals were counted in this case study led by Alexander Seymour and his team at the Duke University Marine Laboratory, North Carolina, USA and Fisheries and Oceans Canada. The locations of the identified grey seals are available through the OBIS web site titled “Atlantic grey seal breeding colonies in Hay and Saddle Islands, Nova Scotia” at http://iobis.org/explore/#/dataset/4534. The more detailed information, georeferenced RGB pictures and thermal images are available through the OBIS-SEAMAP web site at http://seamap.env.duke.edu/dataset/1462.

Reference: Seymour, A., Dale, J., Hammill, M., Halpin, P and Johnston, D. 2017. Automated detection and enumeration of marine wildlife using unmanned aircraft systems (UAS) and thermal imagery. Scientific Reports. 7: 45127. https://www.nature.com/articles/srep45127.

Identifying relevant predictors for marine species distribution modelling with MarineSPEED

species distribution modelling predictor selection OBIS data

Climatological conditions are currently changing at an unprecedented rate and anthropogenic activities displace species out of their native area across the globe. Both processes have the potential to alter biological communities and reduce ecosystem services. Knowing under which environmental conditions species may maintain or establish viable populations therefore is more critical than ever. Species distributions are increasingly modelled for conservation and ecological purposes. A better understanding of mechanisms shap- ing species distributions allows for more accurate predictions of future distributions of species in a rapidly changing world.

Thanks to the availability of an increasing number of online distribution records (e.g., OBIS, GBIF), pre-processed environmental data layers (e.g., WorldClim, Climond, Bio-ORACLE, MARSPEC) and modelling algorithms accessible through various statistical packages, SDM has become a widely applied technique in ecology and conservation biology.

Altough the importance for SDM of selecting biologically relevant predictors, and its impact on model uncertainty and transferability has been highlighted by several studies, to date no comprehensive study on the relevance of the predictors of marine species distributions across taxa has been performed.

In this study, Bosch et al. (2017) created the Marine SPEcies with Environmental Data (MarineSPEED) dataset and used it to: (1) identify the most relevant predictors of marine species distributions and (2) identify which parts of the SDM process impact the relevance of predictors the most.

For MarineSPEED, 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.

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 http://marinespeed.org.

Full reference:

  • Bosch S., Tyberghein L., Deneudt K., Hernandez F., & De Clerck O. (2018) In search of relevant predictors for marine species distribution modelling using the MarineSPEED benchmark dataset. Diversity and Distributions, 24. http://dx.doi.org/10.1111/ddi.12668

More use cases...