Vlaams Instituut voor de Zee
WoRMS : http://www.marinespecies.org/ (World Reg. Mar. Species)
ERMs : http://www.marbef.org/data/erms.php (Eur. Reg. Mar. Species)
BeRMs : http://www.marinespecies.org/berms/ (Belgian Reg. Mar. Species)
Without zooplankton, life on earth would be completely different. The central location in the ecosystem and the important functions in carbon storage make these organisms extremely important for all life on earth. Changes within the zooplankton population would lead to massive mortality in the ecosystems and would mean an extreme increase of carbon dioxide in the atmosphere. Climate change issues, overfishing and the construction of windmills are putting zooplankton species in danger. Rising ocean temperature and ocean acidification would mean the end of zooplankton.
Due to its high sensitivity and extremely important functions, zooplankton is seen as one of the most important indicators of climate change. This is why monthly samples are taken at various locations in the Belgian part of the North Sea by 'het Vlaams Instituut voor de Zee'. These locations are called ‘stations’. One sample is taken at each station to show the population at the sampling location. The extreme variations in densities between stations make it necessary to check whether these variations also occur at a smaller distance from each other. If these variations occur inside a station, the results of the last few years are inaccurate or even wrong, and a sample would be a 'lucky shot’. To check this, five replicates were taken at two different stations.
The samples were analyzed with the ZooSCAN, a revolutionary device to analyze zooplankton samples. Each replicate was analyzed five times in order to be able to identify variations by analyzing them. Using statistical data processing, the variations caused during the analysis can be compared with the variations between the replicates and between the different stations in the same month. In the ideal case, replicates are completely the same and the spread would be similar to the variation caused during the analysis.
The results indicate that the variation between the replicates in the large groups are somewhat higher than the variations observed during analysis. The higher variation has a natural cause and implies that the densities of these species fluctuate somewhat inside a station. For the smaller groups, the results are more encouraging, and the variations are no more significant than those observed during the analyses. This means that the observation of these species is done correctly. When the variations of the replicates are compared with the fluctuations between the different stations, the spread proves to be a lot smaller for the replicates.
It can be concluded that one sampling gives a good indication of the composition of the zooplankton population in the sampling site. Extra samples inside a station or extra analyses with the ZooSCAN can make the results more accurate.
A distribution map is being developed which combines the occurrence records of different databases to study the distribution of marine species and to also see the links between these databases.
The project is envisioned as follows. Using R, all the occurrence records are downloaded from the WoRMS, OBIS, EurOBIS, GBIF and IDIGBIO databases. These are then combined in to a MongoDB database, along with the shapefile of Marine Regions (MR) and Marine Ecoregions Of the World (MEOW). Each shapefile contains a list of polygons, which are linked to the occurrence records via their (latitude/longitude) coordinates. The records also contain a date/time value in most cases. Through an R Shiny app the user can choose the location, taxon and a date range as input. The R Shiny app will query the MongoDB database for the right records and polygons, which will then be plotted on a leaflet map as points and polygons. The infrastructure of this distribution map will be added to the Lifewatch R Shiny projects and to the WoRMS distribution map.
In this traineeship the download is still in progress. The code to download the records is written and is now being applied to download all the records. The MongoDB structure has also been determined.
Further steps of the project, which is the R Shiny app and integration into Lifewatch and WoRMS are still to be done.
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