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GBIF’s occurrence search is a powerful and versatile tool for accessing GBIF mediate data. This vignette will provide an overview of the occ_search() function and provide examples and advice of how to use it effectively and also when not to use it.

The function occ_search() (and related legacy function occ_data()) should not be used for serious research. Users sometimes find it easier to use occ_search() rather than occ_download() because they do not need to supply a username or password, and also do not need to wait for a download to finish. However, any serious research project should always use occ_download() instead.

occ_search() is a quick way to get a non-random sample of occurrences from the GBIF mediated data. It is useful for quickly exploring the data, but it is not suitable for serious research because users are limited to 100,000 records per search combination.

And, even if your search returns fewer than 100,000 records, it is still not recommended to use occ_search() to retrieve all the records for a serious research project. This is because it is not possible to cite the data obtained this way in an easy way.

Here are some examples of some good usages of occ_search():

  • Quickly exploring occurrence data
  • Getting occurrence counts and statistics (see also occ_count() and article here)
  • Testing out search parameters before downloading data

And here are some examples of bad usages of occ_search():

  • Looping through a large number of species to extract occurrence data (See article here instead)
  • Treating the data as a random sample
  • Using occ_search() data for citable research


One of the more useful fields to search on is basisOfRecord, which gives roughly the origin of the occurrence record. Most records on GBIF are either PRESERVED_SPECIMEN (museum/herbarium records) or HUMAN_OBSERVATION (usually citizen science, but sometimes research observations).

Other interesting basisOfRecord values are FOSSIL_SPECIMEN and LIVING_SPECIMEN (zoos or botanical gardens), because people typically want to exclude these from their downloads.

Keep in mind that the basisOfRecord values are not guaranteed to be filled in accurately by the publisher. Sometimes records are misclassified or given a basisOfRecord that you would not expect or have a complicated provenance.

occ_search(basisOfRecord="PRESERVED_SPECIMEN") # museum and herbarium records
occ_search(basisOfRecord="HUMAN_OBSERVATION") # citizen science and research observations
occ_search(basisOfRecord="FOSSIL_SPECIMEN") # fossil records
occ_search(basisOfRecord="LIVING_SPECIMEN") # zoo and botanical garden records
occ_search(basisOfRecord="PRESERVED_SPECIMEN;HUMAN_OBSERVATION") # museum/herbarium and citizen science/research observations
occ_search(basisOfRecord="MACHINE_OBSERVATION") # machine observations (e.g. camera traps, acoustic recorders, etc.)

Searching with scientificName

Users are sometimes attracted to occ_search() because it is possible to supply a scientificName rather than a taxonKey. Note, that in the background a call is made the species match service (similar to name_backbone()) in order to retrieve a GBIF taxonKey. Because of this, a user can sometimes rarely receive back poorly matched occurrences, particularly if authorship is not supplied.

occ_search(scientificName="Caloptery splendens")
# Or better 
occ_search(scientificName="Calopteryx splendens (Harris, 1780)")

Is equivalent to doing the following:

occ_search(taxonKey=name_backbone("Calopteryx splendens")$usageKey)
# OR

If your name happens to be a homotypic synonym of another name, you may get back occurrences for the other name or no results or a higher-rank match results. Therefore, it is usually safer to use the GBIF taxonKey.

Non-interpreted fields

Some fields in the GBIF mediated data are “interpreted” by GBIF, meaning that they are standardized in some way. For example, the field basisOfRecord is standardized to a controlled vocabulary. Therefore, only a few values are returned no matter what the publisher has supplied. For instance, “pinned insect”, “fish specimen”, and “herbarium sheet”, will all get mapped to PRESERVED_SPECIMEN by GBIF.

Other fields are “non-interpreted”, meaning that they are not standardized in any way. For example, the field recordedBy is a free text field. If you search for recordedBy="John Smith", you may not get back occurrences where the recordedBy field is some variant such as J. Smith, Smith, J., Smith, John, etc.

One strategy for determining whether a search term is free text is by using occ_count(facet=<"search term">). See article of occ_count() here.


If many unique values are returned, then it is likely that the field is free text.

Un-intentional mass data removal from NULL values

Some search parameters are often NULL or not supplied from the publisher. In general, occ_search() terms that are not required fields or not filled by GBIF during interpretation are often NULL. For example, even though coordinateUncertaintyInMeters theoretically applies to all occurrences with coordinates, it is often NULL because the publishers choose not to supply this information or it is unknown. Similarly, sex might often be left NULL more than what would be expected naively.

Other columns with more NULLs than one might expect :

  • stateProvince
  • elevation
  • establishmentMeans
  • coordinateUncertaintyInMeters

Keep in mind that specifying any filter will remove all records with NULL in the filter.

Searching for locations

Location searching can sometimes be challenging for new users. Particularly, searching for stateProvince can be tricky because the field is free text when one might expect it to be from a controlled vocabulary. stateProvince="California" will not return occurrences where the publisher supplied has values such as CA, Calif., or Cal.. Additionally, records with coordinates falling within California may not have been supplied with a stateProvince value by the publisher.

occ_search(stateProvince="CA")) # will return different number of records  
occ_search(stateProvince="CA;California")) # search both variants at the same time  

A usually better choice than searching by stateProvince is to search by gadmGid. The term gadmGid is a GBIF interpreted field that is filled by GBIF when coordinates are available. Looking up the gadmGids can be done on the GBIF occurrence search page.

occ_search(gadmGid="USA.5_1") # search for California 
occ_search(gadmGid="JPN.12_1") # search for Hokkaido Japan
occ_search(gadmGid="USA.5_1;USA.6_1") # search for California and Colorado 
occ_search(gadmGid="PHL.10_1") # Bataan Philippines
occ_search(gadmGid="USA") # United States "just land" without EEZ area

Searching by country is typically straightforward because the field is standardized and filled in by GBIF when coordinates are available. Two letter country codes are used when searching occurrences. These codes can be looked up using enumeration_country().

occ_search(country="US") # search for United States
occ_search(country="JP") # search for Japan
occ_search(country="PH") # search for Philippines
occ_search(country="SW") # search for Sweden
occ_search(country="US;JP") # search for United States and Japan

Searching by continent is also possible, but unlike country, this value is not filled in when coordinates are available, and instead relies on the publisher filling in this field. So if the publisher has not filled in a value, then this field will be NULL, even if it obviously lies on a continent.

The field is however standardized by GBIF, so that the values are mapped to supplied values are all mapped to a controlled vocabulary(e.g. “Europa, Euroopa,EUR,Eu” -> EUROPE, “Afrique,”Afr.”,“AF” -> AFRIKA).

occ_search(continent="EUROPE") # search for Europe
occ_search(continent="AFRIKA") # search for Africa
occ_search(continent="EUROPE;AFRIKA") # search for Europe and Africa

If you need to get all occurrences from a certain continent, I would use the gadmGid filter or supply a bounding box or WKT polygon to geometry. When using geometry make sure that your polygon is wound in the correct order (anti-clockwise). When in doubt, using the GBIF web UI to draw and debug the polygon can be a good option. Only POLYGON and MULTIPOLYGON are accepted WKT types.

occ_search(geometry="POLYGON((13.42436 69.86167,4.6469 67.01976,-8.26114 67.2205,-19.62021 67.81281,-28.39768 64.25374,-27.88135 53.09437,-17.55493 44.99691,-16.52228 30.81969,3.61426 32.57676,19.62021 30.37524,38.72411 32.14062,54.21375 33.87246,66.60546 43.14228,72.80133 50.54193,70.21972 62.16009,38.20778 72.6752,23.23447 73.42765,13.42436 69.86167))") # rough polygon around Europe

Sometimes it can be useful to select everything but a certain region, also known as a “polygon with hole in it”. This can be done by formatting your WKT with enough interpolated points.

(-180 -90,-90 -90,0 -90,90 -90,180 -90,180 90,90 90,0 90,-90 90,-180 90,-180 -90),
(-5 -5,-5 5,5 5,5 -5,-5 -5)

Searching for dates

Some records on GBIF can be quite old (1600s), so it is sometimes useful to filter by year to remove these records. Year is typically the collection event or the observation event of the record. Almost all occurrences on GBIF supply a year value. Therefore filtering by year is typically safe from un-intentional mass data filtering from NULL values.

occ_search(year=1998) # search for occurrences from 1998
occ_search(year="1998,2024") # search for occurrences from 1998-2024
occ_search(year="1900;2000") # search for occurrences from 1900 and 2000
occ_search(year="1950,2024") # search for somewhat modern records

Other record ids

Sometimes users are coming to GBIF looking for a specific museum record, but they don’t know the gbifid of the record. In these cases, searching by occurrenceId, catalogNumber, recordNumber or institutionCode can be useful. Keep in mind that many of these fields and may not be unique across all of GBIF. For example, a few institutions might use the same institutionCode, but actual be different institutions. Usually combining a few of these values can get you close to the record you are looking for.

occ_search(catalogNumber="KU 110")

DWCA extensions

New users might not be aware that some data publishers supply additional data beyond simple “when-what-where” data. Richer extra data usually comes in the form of dwcaExtensions. While occ_search() does not return the values from these extensions, it is possible to filter by extension type to see what dataset publishers have published extensions of interest.


Further reading

GBIF tech docs