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Marine World Heritage Sites
The Flanders Marine Institute (VLIZ) is looking for a senior computer scientist (m/f/x, 100% employment, permanent contract) for immediate employment to support us at the UNESCO/IOC Project Office for IODE in Oostende (Belgium).
The US Integrated Ocean Observing System (IOOS) and the Ocean Biogeographic Information System (OBIS) organized a training workshop to develop a community of practice around the management and analysis of biological ocean observing data. Outcomes from the workshop include a collection of software and scripts available on a GitHub repository to aid in the curation of biological data and an expanded network of IOOS, Canadian and OBIS collaborators that are all motivated to expand the global repository of marine biodiversity information. The training materials are also available on the OceanTeacher website.
OBIS training USA
26 participants from 15 countries were trained in all aspects of marine species distribution modelling from data collections to model evaluation and presentation were discussed. The training course was organized by FEPS, OBIS and SEF and funded through the IOC's OceanTeacher Global Academy and all the training material is available online.
OBIS training Belgium
The Marine Biological Association (MBA) joined the OBIS network as the OBIS node in the UK.
UK OBIS node
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.
OBIS training Kenya
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!
OBIS nodes Colombia
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.
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.
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.
- 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