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Data Series
The input data is a combination of different zooplankton datasets linked with EMODnet Biology, and EMODnet or external environmental layers.
All these dataset will be integrated in the EMODnet Biology database
Data Product
The data product is a gridded data product for 40 zooplankton species using DIVAnd (Barth et al., 2014; Troupin et al., 2012) and the neural network library Knet (Yuret, 2016). The neural network uses the variables dissolved oxygen, salinity, temperature, chlorophyll concentration bathymetry and the distance from coast as input. Additionally the position (latitude and longitude) and the year are provided to the neural network.
For example, the maps below show the abundance of Acartia (Acartiura) longiremis (see species description in WoRMS) in the Baltic Sea from 2007 to 2013. A subtle decline can be observed in the Kattegat. The color in the small circles represent actual observations, so circles that blend in the surrounding color means that the result of the neural network interpolation was successful.
Maps showing the abundance of Acartia (Acartiura) longiremis in the Baltic Sea from 2007 to 2013
Abundance values in the NetCDF files are expressed in number per m² and transformed by the function log(x/a + 1) where a is 1 m⁻².
The covers the area from 9°E to 30.8°E and 53°N to 66.1°N at a resolution of a tenth of a degree. Gridded data product for the years 2007, 2008, 2010, 2011, 2012 and 2013 have been made. No observations were available for the year 2009. The fields represent the yearly average abundance.
For every species the correlation length and signal to noise ratio is estimated using the spatial variability of the observations.
The interpolation error is computed using the so-called "clever poor man's error" (Beckers et al., 2014).
The full list of the species is:
Acartia (Acanthacartia) bifilosa
Acartia (Acanthacartia) tonsa
Acartia (Acartiura) clausi
Acartia (Acartiura) longiremis
Amphibalanus improvisus
Appendicularia
Bivalvia
Bosmina (Eubosmina) coregoni
Bryozoa
Calanus finmarchicus
Centropages
Centropages hamatus
Cercopagis (Cercopagis) pengoi
Cnidaria
Cyclopoida
Daphnia
Daphnia cristata
Echinodermata
Eurytemora
Evadne nordmanni
Fritillaria borealis
Gastropoda
Harpacticoida
Keratella cochlearis
Keratella cruciformis
Keratella eichwaldi
Keratella quadrata
Limnocalanus macrurus macrurus
Mysidae
Oikopleura
Oithona
Paracalanus
Pleopis polyphemoides
Podon intermedius
Podon leuckartii
Polychaeta
Pseudocalanus
Rotifera
Synchaeta
Temora longicornis
More Information
References
A. Barth, J.-M. Beckers, C. Troupin, A. Alvera-Azcárate, and L. Vandenbulcke. divand-1.0: n-dimensional variational data analysis for ocean observations. Geoscientific Model Development, 7(1):225–241, 2014. http://doi.org/10.5194/gmd-7-225-2014.
C. Troupin, A. Barth, D. Sirjacobs, M. Ouberdous, J.-M. Brankart, P. Brasseur, M. Rixen, A. Alvera-Azcárate, M. Belounis, A. Capet, F. Lenartz, M.-E. Toussaint, and J.-M. Beckers. Generation of analysis and consistent error fields using the Data Interpolating Variational Analysis (DIVA). Ocean Modelling, 52–53:90–101, 2012. doi.org/10.1016/j.ocemod.2012.05.002.
Yuret, D. Knet: beginning deep learning with 100 lines of julia. In Machine Learning Systems Workshop at NIPS 2016. https://pdfs.semanticscholar.org/28ee/845420b8ba275cf1d695fbf383cc21922fbd.pdf
J.-M. Beckers, A. Barth, C. Troupin, and A. Alvera-Azcárate. Approximate and efficient methods to assess error fields in spatial gridding with data interpolating variational analysis (DIVA). Journal of Atmospheric and Oceanic Technology, 31:515–530, 2014. http://doi.org/10.1175/JTECH-D-13-00130.1.
Code and methodology
https://github.com/EMODnet/EMODnet-Biology-Zooplankton-Baltic
Citation and download link
Barth, A.; Herman, P.M.J.; (2018): Neural network modelling of Baltic zooplankton abundances. Marine Data Archive. https://doi.org/10.14284/381
Authors
Biological data preprocessing by Peter M.J. Herman, Neural network modelling by Alexander Barth