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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

Call to Contribute to Global Harmful Algal Bloom Status Reporting

16 HAB experts from 13 countries were trained in OBIS data entry at the UNESCO-IOC headquarters in Belgium. Based on this training, the effort of compiling and increasing data sets is being intensified in order to provide a substantial part of the basis for a first Global HAB Status Report. This report series will provide the scientific community as well as decision makers with a reference on HAB occurrence and impacts on ecosystem services.

November 03, 2017 - Hallegraeff et al.HAB training

OBIS Training course Kuala Terrenganu, Malaysia, 22-26 October 2017

16 researchers from 8 countries in S-E Asia were trained in OBIS at UNESCO-IOC's OceanTeacher Regional Training Centre in Malaysia. This is one of eight OBIS training courses in 2017, making use of IOC's OceanTeacher Global Academy learning platform. New training material was developed (including many R scripts) and is available online.

October 30, 2017 - Ward AppeltansOBIS training Malaysia

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Use cases

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

Illuminating the Twilight Zone - expert panel maps the world's mesopelagic zone

Biogeography OBIS data

The mesopelagic, or “twilight” zone (open ocean waters between 200 – 1000 m depth), is the world’s second-largest cumulative ecosystem, trailing only the bathypelagic zone (waters > 1000 m depth). In this zone there is not enough sunlight to support photosynthesis (i.e., less than 1% of surface irradiance), but enough light that animals can detect the difference between night and day. The importance of deep-pelagic ecosystems in global ecosystem functioning, such as carbon cycling, is widely acknowledged, but poorly understood. To date less than 1% of this habitat has been sampled, hampering statistical approaches to map its inhabitants on a global scale. A recent paper provides a synthesis of what is known about the distribution of life in the Twilight Zone. Experts integrated available biological data with physical oceanographic spatial modelling to present a biogeographic classification of this massive ecosystem. Thirty-three global ecoregions were identified, of which 20 were truly oceanic, while 13 were ‘distant neritic.’ Each ecoregion harbors a characteristic combination of organisms, with ‘boundaries’ between ecoregions being more like gradients than sharp discontinuities. Each ecoregion is driven by a complex of driving factors, but some of the most important are phytoplankton production in the overlying waters, the presence or absence of oxygen minimum strata, upwelling, and water column stratification. Much work needs to be done to produce a truly dynamic mesopelagic biogeography – huge sections of the World Ocean are still unsampled, and seasonal sampling is rare in all but a few locations. As resource extraction from the deep increases, so too does the need for baseline information to assess human impacts. The proposed mesopelagic classification addresses a standing data gap in global ocean management and conservation efforts.

Full reference:

  • Sutton, T.T., M.R. Clark, D.C. Dunn, P.N. Halpin, A.D. Rogers, J. Guinotte, S.J. Bograd, M.V. Angel, J.A.A. Perez, K. Wishner, R.L. Haedrich, D.J. Lindsay, J.C. Drazen, A. Vereshchaka, U. Piatkowski, T. Morato, K. Błachowiak-Samołyk, B.H. Robison, K.M. Gjerde, A. Pierrot-Bults, P. Bernal, G. Reygondeau. and M. Heino (2017) A global biogeographic classification of the mesopelagic zone. Deep Sea Research I 126: 85-102. https://doi.org/10.1016/j.dsr.2017.05.006

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