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***product** - Output product files: `netCDF` containing the gridded, probability fields and the corresponding figures in `PNG` or `JPG` format.
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***scripts** - Reusable code: functions employed in the Jupyter notebooks.
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## Data
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## Data series
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The main data file is `spe.csv`, it contains all sampling events as rows, and the most frequent species (occurrence >200) as columns. The first columns specify sampling event and location. All columns are coded as __‘pa’__ (for presence/absence) followed by the `AphiaID` of the species. In the separate file `specieslist.csv` you find the names of the species corresponding to their `AphiaID`. You also find the total number of events where the species has been found.
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The input data was provided by [Herman et al (2020)](https://www.emodnet-biology.eu/data-catalog?module=dataset&dasid=6617). The main data file is `spe.csv`, it contains all sampling events as rows, and the most frequent species (occurrence >200) as columns. The first columns specify sampling event and location. All columns are coded as __‘pa’__ (for presence/absence) followed by the `AphiaID` of the species. In the separate file `specieslist.csv` you find the names of the species corresponding to their `AphiaID`. You also find the total number of events where the species has been found.
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A species can have value __1__ (present), __0__ (looked for but absent) and __NA__ (not looked for). The NA values must be disregarded when making maps. They do not contain information about either presence or absence. There are NAs in the file because some datasets only look for a subset of species.
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The information about the occurrence of a species is obtained with the function
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`read_coords_species` defined in the script [`BenthosInterp.jl`](scripts/BenthosInterp.jl). The species are selected by using the _AphiaID_.
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## Analysis
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## Data product
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This directory contains the notebooks for the preparation and analysis of the data, as well as the generation of the figures.
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@@ -67,43 +67,28 @@ If we have only presence data, the 2nd term of the denominator is zero and
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the probability reduces to the presence heatmap.
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The absence data decrease the ratio and hence the probability.
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### Products
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Two types of analysis are performed:
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1. Using a uniform correlation length all over the domain (0.1°) (directory `1-UniformL`)
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2. Using a spatially variable correlation length (see figure below), derives from the substrates (directory `2-VariableL`).
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For both analysis, 2 types of products are created (described in the next section):
2. read the coordinates of presence and absence for each species,
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3. compute the probability map and the associated error field and
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4. write the results in a netCDF file (one per species and one global for all
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the species), in the directory `product/netCDF`
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the species), in the directory `product/netCDF`
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*[`plot_results_map.ipynb`](analysis/plot_results_map.ipynb): notebook in `Python` to create the figures using the _ETRS89 Lambert Azimuthal Equal Area_ coordinate reference system of 2001 (`EPGS 3035`).
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The figures are stored in `product/figures` and consist of
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The figures are stored in `product/figures` and consist of
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1. The interpolated probability map
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2. The relative error field
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3. The interpolated probability masked by the error field: for some species the
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computed probability displays high and non-realistic values, due to the ratio of very small (but different) values in regions without observations.
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computed probability displays high and non-realistic values, due to the ratio of very small (but different) values in regions without observations.
if plots are to be created with Julia, those packages are also required:
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```julia
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Pkg.add("PyCall")
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Pkg.add("PyPlot")
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Pkg.add("Proj4")
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```
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## Citation
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### Products
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Two types of analysis are performed:
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1. Using a uniform correlation length all over the domain (0.1°) (directory `1-UniformL`)
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2. Using a spatially variable correlation length (see figure below), derives from the substrates (directory `2-VariableL`).
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For both analysis, 2 types of products are created (described in the next section):
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1. Figures (`jpg` format).
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2. NetCDF files.
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## More information
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### References
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Barth, A., Beckers, J.-M., Troupin, C., Alvera-Azcárate, A., and Vandenbulcke, L.(2014): divand-1.0: *n*-dimensional variational data analysis for ocean observations, Geosci. Model Dev., 7, 225–241, https://doi.org/10.5194/gmd-7-225-2014.
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Herman, P.M.J., Stolte, W., van der Heijden, L. 2020. Summary presence/absence maps of macro-endobenthos in the greater North Sea, based on nearly 100,000 samples from 65 assembled monitoring data sets. Integrated data products created under the European Marine Observation Data Network (EMODnet) Biology project (EASME/EMFF/2017/1.3.1.2/02/SI2.789013), funded by the European Union under Regulation (EU) No 508/2014 of the European Parliament and of the Council of 15 May 2014 on the European Maritime and Fisheries Fund. Available at: https://www.emodnet-biology.eu/data-catalog?module=dataset&dasid=6617
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### Code and methodology
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#### Code
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The code, written in `Julia`, is distributed through GitHub:
*A. Barth, P. Hermann and C. Troupin (2020). Probability maps
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for different benthos species in the North Sea. Integrated data products created under the European Marine Observation Data Network (EMODnet) Biology project (EASME/EMFF/2017/1.3.1.2/02/SI2.789013), funded by the European Union under Regulation (EU) No 508/2014 of the European Parliament and of the Council of 15 May 2014 on the European Maritime and Fisheries Fund.*
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The code is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.
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The product is available to download at: https://www.emodnet-biology.eu/data-catalog?module=dataset&dasid=6640
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### Authors
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[A. Barth](https://www.emodnet-biology.eu/data-catalog?module=person&persid=5344), [P. Hermann](https://www.emodnet-biology.eu/data-catalog?module=person&persid=79) and [C. Troupin](https://www.emodnet-biology.eu/data-catalog?module=person&persid=19092)
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### Acknowledgement
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Computational resources have been provided by the *Consortium des Équipements de Calcul Intensif* (CÉCI), funded by the Fonds de la Recherche Scientifique de Belgique (F.R.S.-FNRS) under Grant No. 2.5020.11 and by the Walloon Region"
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