The eurobis R package allows you to download data from EurOBIS.
You can query on:
- Dataset: provide the Integrated Marine Information System (IMIS) unique identifier for datasets DasID.
- Taxon: use a scientific name (e.g. the sea turtle Caretta caretta) or a WoRMS AphiaID (e.g. 137205)
- Traits: get all occurrences that are benthos. Or zooplankton. Or both. Powered by WoRMS.
- Time: just give start and end dates.
- Geographically: it allows to query on more than 300 records from the Marine Regions Gazetteer by giving the MRGID. Or just pass the area of your interest as a polygon written in as Well Known Text
- Other important classifications as IUCN Red List, MSDF Indicators or Habitats Directive and CITES Annexes.
Or create your own selection using the EurOBIS toolbox. Just copy the webservice URL and paste in R.
Installation
You can install the development version from GitHub with devtools:
devtools::install_github("lifewatch/eurobis")Get occurrences
Use the eurobis_occurrences() family of functions to query data.
A basic example is:
library(eurobis)
# Get one single dataset
eurobis_occurrences_basic(dasid = 8045)
#> Loading ISO 19139 XML schemas...
#> Loading ISO 19115 codelists...
#> Loading IANA mime types...
#> No encoding supplied: defaulting to UTF-8.
#> ✔ Downloading layer: EMODnet EurOBIS Basic Occurrence Data
#> ℹ The Basic Occurrence Data download provides you data for the following 8 essential terms: datasetid, datecollected, decimallongitude, decimallatitude, coordinateuncertaintyinmeters, scientificname, aphiaid, scientificnameaccepted. For more information, please consult: https://www.emodnet-biology.eu/emodnet-data-format.
#> Simple feature collection with 52 features and 10 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: -8.73964 ymin: 40.61773 xmax: -8.73902 ymax: 40.61899
#> CRS: EPSG:4326
#> # A tibble: 52 × 11
#> gml_id id datas…¹ datecollected decim…² decim…³ coord…⁴ scien…⁵
#> * <fct> <int> <chr> <dttm> <dbl> <dbl> <dbl> <chr>
#> 1 eurobis-o… 3.68e7 https:… 2020-05-05 00:00:00 -8.74 40.6 5 Zoster…
#> 2 eurobis-o… 3.68e7 https:… 2019-12-13 00:00:00 -8.74 40.6 5 Zoster…
#> 3 eurobis-o… 3.68e7 https:… 2020-03-03 00:00:00 -8.74 40.6 5 Zoster…
#> 4 eurobis-o… 3.68e7 https:… 2019-12-13 00:00:00 -8.74 40.6 5 Zoster…
#> 5 eurobis-o… 3.68e7 https:… 2020-08-10 00:00:00 -8.74 40.6 5 Zoster…
#> 6 eurobis-o… 3.68e7 https:… 2020-08-10 00:00:00 -8.74 40.6 5 Zoster…
#> 7 eurobis-o… 3.68e7 https:… 2020-05-05 00:00:00 -8.74 40.6 5 Zoster…
#> 8 eurobis-o… 3.68e7 https:… 2020-05-05 00:00:00 -8.74 40.6 5 Zoster…
#> 9 eurobis-o… 3.68e7 https:… 2020-03-03 00:00:00 -8.74 40.6 5 Zoster…
#> 10 eurobis-o… 3.68e7 https:… 2020-05-05 00:00:00 -8.74 40.6 5 Zoster…
#> # … with 42 more rows, 3 more variables: aphiaid <chr>,
#> # scientificnameaccepted <chr>, geometry <POINT [°]>, and abbreviated
#> # variable names ¹datasetid, ²decimallongitude, ³decimallatitude,
#> # ⁴coordinateuncertaintyinmeters, ⁵scientificnameFor detailed information run:
help(eurobis_occurrences)Query by traits
Use the functional_group argument:
# Get one single dataset
eurobis_occurrences_basic(dasid = 8045, functional_groups = "angiosperms")See the full list of queriable traits in the exported dataset species_traits:
species_traits
#> category group
#> 1 Species group Functional group
#> 2 Species group Functional group
#> 3 Species group Functional group
#> 4 Species group Functional group
#> 5 Species group Functional group
#> 6 Species group Functional group
#> 7 Species group Functional group
#> 8 Species group Functional group
#> 9 Species group Functional group
#> 10 Species importance to society CITES Annex
#> 11 Species importance to society CITES Annex
#> 12 Species importance to society CITES Annex
#> 13 Species importance to society Habitats Directive Annex
#> 14 Species importance to society Habitats Directive Annex
#> 15 Species importance to society IUCN Red List Category
#> 16 Species importance to society IUCN Red List Category
#> 17 Species importance to society IUCN Red List Category
#> 18 Species importance to society MSFD indicators
#> 19 Species importance to society MSFD indicators
#> 20 Species importance to society MSFD indicators
#> 21 Species importance to society MSFD indicators
#> 22 Species importance to society MSFD indicators
#> 23 Species importance to society MSFD indicators
#> 24 Species importance to society MSFD indicators
#> 25 Species importance to society MSFD indicators
#> 26 Species importance to society MSFD indicators
#> 27 Species importance to society MSFD indicators
#> 28 Species importance to society MSFD indicators
#> 29 Species importance to society MSFD indicators
#> 30 Species importance to society MSFD indicators
#> 31 Species importance to society MSFD indicators
#> 32 Species importance to society MSFD indicators
#> selection
#> 1 algae
#> 2 angiosperms
#> 3 benthos
#> 4 birds
#> 5 mammals
#> 6 phytoplankton
#> 7 pisces
#> 8 reptiles
#> 9 zooplankton
#> 10 I
#> 11 II
#> 12 III
#> 13 II
#> 14 IV
#> 15 data deficient
#> 16 least concern
#> 17 near threatened
#> 18 Black Sea proposed indicators
#> 19 HELCOM core biodiversity indicators
#> 20 Mediterranean proposed indicators - Adriatic Sea
#> 21 Mediterranean proposed indicators - Aegean-Levantine Sea
#> 22 Mediterranean proposed indicators - Ionian Sea
#> 23 Mediterranean proposed indicators - Mediterranean Sea
#> 24 Mediterranean proposed indicators - Western Mediterranean
#> 25 OSPAR candidate indicators: Bay of Biscay and the Iberian Coast
#> 26 OSPAR candidate indicators: Celtic Seas
#> 27 OSPAR candidate indicators: Greater North Sea including outside EU
#> 28 OSPAR candidate indicators: North Sea
#> 29 OSPAR common indicators: Bay of Biscay and Iberian Coast
#> 30 OSPAR common indicators: Celtic Seas
#> 31 OSPAR common indicators: Greater North Sea
#> 32 OSPAR common indicators: Greater North Sea including outside EU
#> selectid
#> 1 Algae
#> 2 Angiosperms
#> 3 Benthos
#> 4 Birds
#> 5 Mammals
#> 6 phytoplankton
#> 7 pisces
#> 8 Reptiles
#> 9 zooplankton
#> 10 28_280_0
#> 11 28_281_0
#> 12 28_282_0
#> 13 26_269_0
#> 14 26_271_0
#> 15 1_8_3
#> 16 1_7_3
#> 17 1_6_3
#> 18 23_285_41
#> 19 23_285_29
#> 20 23_285_31
#> 21 23_285_32
#> 22 23_285_33
#> 23 23_285_34
#> 24 23_285_30
#> 25 23_285_35
#> 26 23_285_36
#> 27 23_285_37
#> 28 23_285_38
#> 29 23_285_44
#> 30 23_285_45
#> 31 23_285_46
#> 32 23_285_40Query by location
You can also filter by location, either using the Marine Regions Gazetteer Identifier (MRGID) or passing any polygon as Well Known Text.
eurobis_occurrences_basic(mrgid = 5688, geometry = "POLYGON ((-9.099426 40.33016, -9.099426 40.9788, -8.366089 40.9788, -8.366089 40.33016, -9.099426 40.33016))")To help drawing the area of your interest, you can use eurobis_map_draw(). You can draw here a polygon interactively:
selected_area <- eurobis_map_draw()
#> POLYGON ((-9.099426 40.33016, -9.099426 40.9788, -8.366089 40.9788, -8.366089 40.33016, -9.099426 40.33016))
eurobis_occurrences_full(geometry = selected_area)To help choosing the MRGID from Marine Regions, see the list of queriable regions and their MRGIDs with the family of functions eurobis_map_regions_*: these will open a leaflet map including the layers, read via Web Map Services (WMS).
# MEOW Ecoregions
eurobis_map_regions_ecoregions()
# Exclusive Economic Zones (EEZ)
eurobis_map_regions_eez()
# International Hydrographic Office areas (IHO)
eurobis_map_regions_iho()
# Marine Regions intersection of EEZ and IHO. Named as Marine Region in eurobis_list_regions()
eurobis_map_regions_eez_iho()
# EMODnet-Biology Reporting Areas
eurobis_map_regions_reportingareas()See the manual with:
help(eurobis_map_regions)Note that passing both an MRGID and a geometry does not restrict to the selected area within the MRGID record, but adds both data fetched from the selected data and the MRGID record.
Why an EurOBIS R package? Didn’t exist an OBIS package already?
Yes, you could also use the OBIS R package robis. The main advantage of the eurobis R package is the functionality to query on species traits.
Disclaimer
If data are extracted from the EMODnet Biology for secondary analysis resulting in a publication, the appropriate source should be cited.
The downloaded data should be cited as: EMODnet Biology (yyyy) Fulll Occurrence Data and parameters downloaded from the EMODnet Biology Project (www.emodnet-biology.eu). Available online at www.emodnet-biology.eu/toolbox, consulted on yyyy-mm-dd.
Regarding the citation of the individual datasets, the following guidelines should be taken into account:
- If any individual data source of EurOBIS constitutes a significant proportion of the downloaded and used records (e.g. more than 10% of the data is derived from a single source), the individual data source should also be cited.
- If any individual data source of EurOBIS constitutes a substantial proportion of the downloaded and used records (e.g. more than 25% of the data is derived from a single source or the data is essential to arrive at the conclusion of the analysis), the manager or custodian of this data set should be contacted.
- In any case, it may be useful to contact the data custodian directly. The data custodian might have additional data available that may strengthen the analysis or he/she might be able to provide additional helpful information that may not be apparent from the provided metadata.
- The data may not be redistributed without the permission of the appropriate data owners. If data are extracted from the EMODnet Data Portal for redistribution, please contact us at bio@emodnet.eu.
