Search for GBIF occurrences - simplified for speed

occ_data(
  taxonKey = NULL,
  scientificName = NULL,
  country = NULL,
  publishingCountry = NULL,
  hasCoordinate = NULL,
  typeStatus = NULL,
  recordNumber = NULL,
  lastInterpreted = NULL,
  continent = NULL,
  geometry = NULL,
  geom_big = "asis",
  geom_size = 40,
  geom_n = 10,
  recordedBy = NULL,
  recordedByID = NULL,
  identifiedByID = NULL,
  basisOfRecord = NULL,
  datasetKey = NULL,
  eventDate = NULL,
  catalogNumber = NULL,
  year = NULL,
  month = NULL,
  decimalLatitude = NULL,
  decimalLongitude = NULL,
  elevation = NULL,
  depth = NULL,
  institutionCode = NULL,
  collectionCode = NULL,
  hasGeospatialIssue = NULL,
  issue = NULL,
  search = NULL,
  mediaType = NULL,
  subgenusKey = NULL,
  repatriated = NULL,
  phylumKey = NULL,
  kingdomKey = NULL,
  classKey = NULL,
  orderKey = NULL,
  familyKey = NULL,
  genusKey = NULL,
  establishmentMeans = NULL,
  protocol = NULL,
  license = NULL,
  organismId = NULL,
  publishingOrg = NULL,
  stateProvince = NULL,
  waterBody = NULL,
  locality = NULL,
  limit = 500,
  start = 0,
  skip_validate = TRUE,
  curlopts = list()
)

Arguments

taxonKey

(numeric) A taxon key from the GBIF backbone. All included and synonym taxa are included in the search, so a search for aves with taxononKey=212 (i.e. /occurrence/search?taxonKey=212) will match all birds, no matter which species. You can pass many keys by passing occ_search in a call to an lapply-family function (see last example below).

scientificName

A scientific name from the GBIF backbone. All included and synonym taxa are included in the search.

country

The 2-letter country code (as per ISO-3166-1) of the country in which the occurrence was recorded. See here https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2

publishingCountry

The 2-letter country code (as per ISO-3166-1) of the country in which the occurrence was recorded.

hasCoordinate

(logical) Return only occurence records with lat/long data (TRUE) or all records (FALSE, default).

typeStatus

Type status of the specimen. One of many options. See ?typestatus

recordNumber

Number recorded by collector of the data, different from GBIF record number. See http://rs.tdwg.org/dwc/terms/#recordNumber for more info

lastInterpreted

Date the record was last modified in GBIF, in ISO 8601 format: yyyy, yyyy-MM, yyyy-MM-dd, or MM-dd. Supports range queries, smaller,larger (e.g., '1990,1991', whereas '1991,1990' wouldn't work)

continent

Continent. One of africa, antarctica, asia, europe, north_america (North America includes the Caribbean and reachies down and includes Panama), oceania, or south_america

geometry

Searches for occurrences inside a polygon described in Well Known Text (WKT) format. A WKT shape written as either POINT, LINESTRING, LINEARRING POLYGON, or MULTIPOLYGON. Example of a polygon: POLYGON((30.1 10.1, 20, 20 40, 40 40, 30.1 10.1)) would be queried as http://bit.ly/1BzNwDq See also the section WKT below.

geom_big

(character) One of "axe", "bbox", or "asis" (default). See Details.

geom_size

(integer) An integer indicating size of the cell. Default: 40. See Details.

geom_n

(integer) An integer indicating number of cells in each dimension. Default: 10. See Details.

recordedBy

The person who recorded the occurrence.

recordedByID

(character) Identifier (e.g. ORCID) for the person who recorded the occurrence

identifiedByID

(character) Identifier (e.g. ORCID) for the person who provided the taxonomic identification of the occurrence.

basisOfRecord

Basis of record, as defined in our BasisOfRecord enum here https://gbif.github.io/gbif-api/apidocs/org/gbif/api/vocabulary/BasisOfRecord.html Acceptable values are:

  • FOSSIL_SPECIMEN An occurrence record describing a fossilized specimen.

  • HUMAN_OBSERVATION An occurrence record describing an observation made by one or more people.

  • LITERATURE An occurrence record based on literature alone.

  • LIVING_SPECIMEN An occurrence record describing a living specimen, e.g.

  • MACHINE_OBSERVATION An occurrence record describing an observation made by a machine.

  • OBSERVATION An occurrence record describing an observation.

  • PRESERVED_SPECIMEN An occurrence record describing a preserved specimen.

  • UNKNOWN Unknown basis for the record.

datasetKey

The occurrence dataset key (a uuid)

eventDate

Occurrence date in ISO 8601 format: yyyy, yyyy-MM, yyyy-MM-dd, or MM-dd. Supports range queries, smaller,larger (e.g., '1990,1991', whereas '1991,1990' wouldn't work)

catalogNumber

An identifier of any form assigned by the source within a physical collection or digital dataset for the record which may not unique, but should be fairly unique in combination with the institution and collection code.

year

The 4 digit year. A year of 98 will be interpreted as AD 98. Supports range queries, smaller,larger (e.g., '1990,1991', whereas '1991,1990' wouldn't work)

month

The month of the year, starting with 1 for January. Supports range queries, smaller,larger (e.g., '1,2', whereas '2,1' wouldn't work)

decimalLatitude

Latitude in decimals between -90 and 90 based on WGS 84. Supports range queries, smaller,larger (e.g., '25,30', whereas '30,25' wouldn't work)

decimalLongitude

Longitude in decimals between -180 and 180 based on WGS 84. Supports range queries (e.g., '-0.4,-0.2', whereas '-0.2,-0.4' wouldn't work).

elevation

Elevation in meters above sea level. Supports range queries, smaller,larger (e.g., '5,30', whereas '30,5' wouldn't work)

depth

Depth in meters relative to elevation. For example 10 meters below a lake surface with given elevation. Supports range queries, smaller,larger (e.g., '5,30', whereas '30,5' wouldn't work)

institutionCode

An identifier of any form assigned by the source to identify the institution the record belongs to. Not guaranteed to be que.

collectionCode

An identifier of any form assigned by the source to identify the physical collection or digital dataset uniquely within the text of an institution.

hasGeospatialIssue

(logical) Includes/excludes occurrence records which contain spatial issues (as determined in our record interpretation), i.e. hasGeospatialIssue=TRUE returns only those records with spatial issues while hasGeospatialIssue=FALSE includes only records without spatial issues. The absence of this parameter returns any record with or without spatial issues.

issue

(character) One or more of many possible issues with each occurrence record. See Details. Issues passed to this parameter filter results by the issue.

search

Query terms. The value for this parameter can be a simple word or a phrase.

mediaType

Media type. Default is NULL, so no filtering on mediatype. Options: NULL, 'MovingImage', 'Sound', and 'StillImage'.

subgenusKey

(numeric) Subgenus classification key.

repatriated

(character) Searches for records whose publishing country is different to the country where the record was recorded in.

phylumKey

(numeric) Phylum classification key.

kingdomKey

(numeric) Kingdom classification key.

classKey

(numeric) Class classification key.

orderKey

(numeric) Order classification key.

familyKey

(numeric) Family classification key.

genusKey

(numeric) Genus classification key.

establishmentMeans

(character) EstablishmentMeans, possible values include: INTRODUCED, INVASIVE, MANAGED, NATIVE, NATURALISED, UNCERTAIN

protocol

(character) Protocol or mechanism used to provide the occurrence record. See Details for possible values

license

(character) The type license applied to the dataset or record. Possible values: CC0_1_0, CC_BY_4_0, CC_BY_NC_4_0, UNSPECIFIED, and UNSUPPORTED

organismId

(numeric) An identifier for the Organism instance (as opposed to a particular digital record of the Organism). May be a globally unique identifier or an identifier specific to the data set.

publishingOrg

(character) The publishing organization key (a UUID).

stateProvince

(character) The name of the next smaller administrative region than country (state, province, canton, department, region, etc.) in which the Location occurs.

waterBody

(character) The name of the water body in which the locations occur

locality

(character) The specific description of the place.

limit

Number of records to return. Default: 500. Note that the per request maximum is 300, but since we set it at 500 for the function, we do two requests to get you the 500 records (if there are that many). Note that there is a hard maximum of 100,000, which is calculated as the limit+start, so start=99,000 and limit=2000 won't work

start

Record number to start at. Use in combination with limit to page through results. Note that we do the paging internally for you, but you can manually set the start parameter

skip_validate

(logical) whether to skip wicket::wkt_validate call or not. passed down to check_wkt(). Default: TRUE

curlopts

list of named curl options passed on to HttpClient. see curl::curl_options for curl options

Value

An object of class gbif_data, which is a S3 class list, with slots for metadata (meta) and the occurrence data itself (data), and with attributes listing the user supplied arguments and whether it was a "single" or "many" search; that is, if you supply two values of the datasetKey parameter to searches are done, and it's a "many". meta is a list of length four with offset, limit, endOfRecords and count fields. data is a tibble (aka data.frame)

Details

protocol parameter options:

  • BIOCASE - A BioCASe protocl compliant service.

  • DIGIR - A DiGIR service endpoint.

  • DIGIR_MANIS - A DiGIR service slightly modified for the MANIS network.

  • DWC_ARCHIVE - A Darwin Core Archive as defined by the Darwin Core Text Guidelines.

  • EML - A single EML metadata document in any EML version.

  • FEED - Syndication feeds like RSS or ATOM of various flavors.

  • OAI_PMH - The Open Archives Initiative Protocol for Metadata Harvesting.

  • OTHER - Any other service not covered by this enum so far.

  • TAPIR - A TAPIR service.

  • TCS_RDF - Taxon Concept data given as RDF based on the TDWG ontology.

  • TCS_XML - A Taxon Concept Schema document.

  • WFS - An OGC Web Feature Service.

  • WMS - An OGC Web Map Service.

Multiple parmeters: Note that you can pass in a vector to one of taxonKey, scientificName, datasetKey, catalogNumber, recordedBy, geometry, country, publishingCountry, recordNumber, search, institutionCode, collectionCode, decimalLatitude, decimalLongitude, depth, year, typeStatus, lastInterpreted, continent, or mediatype parameters in a function call, but not a vector >1 of these parameters at the same time

Hierarchies: hierarchies are returned wih each occurrence object. There is no option no to return them from the API. However, within the occ_search function you can select whether to return just hierarchies, just data, all of data and hiearchies and metadata, or just metadata. If all hierarchies are the same we just return one for you.

Data: By default only three data fields are returned: name (the species name), decimallatitude, and decimallongitude. Set parameter minimal=FALSE if you want more data.

Nerds: You can pass parameters not defined in this function into the call to the GBIF API to control things about the call itself using curlopts. See an example below that passes in the verbose function to get details on the http call.

Scientific names vs. taxon keys: In the previous GBIF API and the version of rgbif that wrapped that API, you could search the equivalent of this function with a species name, which was convenient. However, names are messy right. So it sorta makes sense to sort out the species key numbers you want exactly, and then get your occurrence data with this function. GBIF has added a parameter scientificName to allow searches by scientific names in this function - which includes synonym taxa. Note: that if you do use the scientificName parameter, we will check internally that it's not a synonym of an accepted name, and if it is, we'll search on the accepted name. If you want to force searching by a synonym do so by finding the GBIF identifier first with any name_* functions, then pass that ID to the taxonKey parameter.

WKT: Examples of valid WKT objects:

  • 'POLYGON((-19.5 34.1, 27.8 34.1, 35.9 68.1, -25.3 68.1, -19.5 34.1))'

  • 'MULTIPOLYGON(((-123 38,-116 38,-116 43,-123 43,-123 38)),((-97 41,-93 41,-93 45,-97 45,-97 41)))'

  • 'POINT(-120 40)'

  • 'LINESTRING(3 4,10 50,20 25)'

  • 'LINEARRING' ???' - Not sure how to specify this. Anyone?

Note that GBIF expects counter-clockwise winding order for WKT. You can supply clockwise WKT, but GBIF treats it as an exclusion, so you get all data not inside the WKT area. occ_download() behaves differently in that you should simply get no data back at all with clockwise WKT.

Long WKT: Options for handling long WKT strings: Note that long WKT strings are specially handled when using occ_search or occ_data. Here are the three options for long WKT strings (> 1500 characters), set one of these three via the parameter geom_big:

  • asis - the default setting. This means we don't do anything internally. That is, we just pass on your WKT string just as we've done before in this package.

  • axe - this option uses the geoaxe package to chop up your WKT string in to many polygons, which then leads to a separate data request for each polygon piece, then we combine all dat back together to give to you. Note that if your WKT string is not of type polygon, we drop back to asisas there's no way to chop up linestrings, etc. This option will in most cases be slower than the other two options. However, this polygon splitting approach won't have the problem of the disconnect between how many records you want and what you actually get back as with the bbox option.

    This method uses chop, which uses GridTopologyfrom the sp package, which has two parameters cellsize and cells.dim that we use to chop up polygons. You can tweak those parameters here by tweaking geom_size and geom_n. geom_size seems to be more useful in toggling the number of WKT strings you get back.

    See wkt_parse to manually break make WKT bounding box from a larger WKT string, or break a larger WKT string into many smaller ones.

  • bbox - this option checks whether your WKT string is longer than 1500 characters, and if it is we create a bounding box from the WKT, do the GBIF search with that bounding box, then prune the resulting data to only those occurrences in your original WKT string. There is a big caveat however. Because we create a bounding box from the WKT, and the limit parameter determines some subset of records to get, then when we prune the resulting data to the WKT, the number of records you get could be less than what you set with your limit parameter. However, you could set the limit to be high enough so that you get all records back found in that bounding box, then you'll get all the records available within the WKT.

Range queries: A range query is as it sounds - you query on a range of values defined by a lower and upper limit. Do a range query by specifying the lower and upper limit in a vector like depth='50,100'. It would be more R like to specify the range in a vector like c(50,100), but that sort of syntax allows you to do many searches, one for each element in the vector - thus range queries have to differ. The following parameters support range queries.

  • decimalLatitude

  • decimalLongitude

  • depth

  • elevation

  • eventDate

  • lastInterpreted

  • month

  • year

Issue: The options for the issue parameter (from https://gbif.github.io/gbif-api/apidocs/org/gbif/api/vocabulary/OccurrenceIssue.html):

  • BASIS_OF_RECORD_INVALID The given basis of record is impossible to interpret or seriously different from the recommended vocabulary.

  • CONTINENT_COUNTRY_MISMATCH The interpreted continent and country do not match up.

  • CONTINENT_DERIVED_FROM_COORDINATES The interpreted continent is based on the coordinates, not the verbatim string information.

  • CONTINENT_INVALID Uninterpretable continent values found.

  • COORDINATE_INVALID Coordinate value given in some form but GBIF is unable to interpret it.

  • COORDINATE_OUT_OF_RANGE Coordinate has invalid lat/lon values out of their decimal max range.

  • COORDINATE_REPROJECTED The original coordinate was successfully reprojected from a different geodetic datum to WGS84.

  • COORDINATE_REPROJECTION_FAILED The given decimal latitude and longitude could not be reprojected to WGS84 based on the provided datum.

  • COORDINATE_REPROJECTION_SUSPICIOUS Indicates successful coordinate reprojection according to provided datum, but which results in a datum shift larger than 0.1 decimal degrees.

  • COORDINATE_ROUNDED Original coordinate modified by rounding to 5 decimals.

  • COUNTRY_COORDINATE_MISMATCH The interpreted occurrence coordinates fall outside of the indicated country.

  • COUNTRY_DERIVED_FROM_COORDINATES The interpreted country is based on the coordinates, not the verbatim string information.

  • COUNTRY_INVALID Uninterpretable country values found.

  • COUNTRY_MISMATCH Interpreted country for dwc:country and dwc:countryCode contradict each other.

  • DEPTH_MIN_MAX_SWAPPED Set if supplied min>max

  • DEPTH_NON_NUMERIC Set if depth is a non numeric value

  • DEPTH_NOT_METRIC Set if supplied depth is not given in the metric system, for example using feet instead of meters

  • DEPTH_UNLIKELY Set if depth is larger than 11.000m or negative.

  • ELEVATION_MIN_MAX_SWAPPED Set if supplied min > max elevation

  • ELEVATION_NON_NUMERIC Set if elevation is a non numeric value

  • ELEVATION_NOT_METRIC Set if supplied elevation is not given in the metric system, for example using feet instead of meters

  • ELEVATION_UNLIKELY Set if elevation is above the troposphere (17km) or below 11km (Mariana Trench).

  • GEODETIC_DATUM_ASSUMED_WGS84 Indicating that the interpreted coordinates assume they are based on WGS84 datum as the datum was either not indicated or interpretable.

  • GEODETIC_DATUM_INVALID The geodetic datum given could not be interpreted.

  • IDENTIFIED_DATE_INVALID The date given for dwc:dateIdentified is invalid and cant be interpreted at all.

  • IDENTIFIED_DATE_UNLIKELY The date given for dwc:dateIdentified is in the future or before Linnean times (1700).

  • MODIFIED_DATE_INVALID A (partial) invalid date is given for dc:modified, such as a non existing date, invalid zero month, etc.

  • MODIFIED_DATE_UNLIKELY The date given for dc:modified is in the future or predates unix time (1970).

  • MULTIMEDIA_DATE_INVALID An invalid date is given for dc:created of a multimedia object.

  • MULTIMEDIA_URI_INVALID An invalid uri is given for a multimedia object.

  • PRESUMED_NEGATED_LATITUDE Latitude appears to be negated, e.g. 32.3 instead of -32.3

  • PRESUMED_NEGATED_LONGITUDE Longitude appears to be negated, e.g. 32.3 instead of -32.3

  • PRESUMED_SWAPPED_COORDINATE Latitude and longitude appear to be swapped.

  • RECORDED_DATE_INVALID A (partial) invalid date is given, such as a non existing date, invalid zero month, etc.

  • RECORDED_DATE_MISMATCH The recording date specified as the eventDate string and the individual year, month, day are contradicting.

  • RECORDED_DATE_UNLIKELY The recording date is highly unlikely, falling either into the future or represents a very old date before 1600 that predates modern taxonomy.

  • REFERENCES_URI_INVALID An invalid uri is given for dc:references.

  • TAXON_MATCH_FUZZY Matching to the taxonomic backbone can only be done using a fuzzy, non exact match.

  • TAXON_MATCH_HIGHERRANK Matching to the taxonomic backbone can only be done on a higher rank and not the scientific name.

  • TAXON_MATCH_NONE Matching to the taxonomic backbone cannot be done cause there was no match at all or several matches with too little information to keep them apart (homonyms).

  • TYPE_STATUS_INVALID The given type status is impossible to interpret or seriously different from the recommended vocabulary.

  • ZERO_COORDINATE Coordinate is the exact 0/0 coordinate, often indicating a bad null coordinate.

Counts: There is a slight difference in the way records are counted here vs. results from occ_count. For equivalent outcomes, in this function use hasCoordinate=TRUE, and hasGeospatialIssue=FALSE to have the same outcome using occ_count with isGeoreferenced=TRUE

This does nearly the same thing as occ_search(), but is simplified for speed, and is for the most common use case where user just wants occurrence data, and not other information like taxon hierarchies and media (e.g., images). Alot of time in occ_search() is used parsing data to be more useable downstream. We do less of that in this function.

There are a number of data fields GBIF returns that we drop to speed up processing time within R. These fields take extra time to process because they are deeply nested and so take extra time to check if they are empty or not, and if not, figure out how to parse them into a data.frame. The fields are:

  • gadm

  • media

  • facts

  • relations

  • extensions

  • identifiers

  • recordedByIDs

  • identifiedByIDs

To get these fields use occ_search() instead.

Note

Maximum number of records you can get with this function is 100,000. See https://www.gbif.org/developer/occurrence

References

https://www.gbif.org/developer/occurrence#search

See also

Examples

if (FALSE) { (key <- name_backbone(name='Encelia californica')$speciesKey) occ_data(taxonKey = key, limit = 4) (res <- occ_data(taxonKey = key, limit = 400)) # Return 20 results, this is the default by the way (key <- name_suggest(q='Helianthus annuus', rank='species')$data$key[1]) occ_data(taxonKey=key, limit=20) # Instead of getting a taxon key first, you can search for a name directly ## However, note that using this approach (with \code{scientificName="..."}) ## you are getting synonyms too. The results for using \code{scientifcName} ## and \code{taxonKey} parameters are the same in this case, but I wouldn't ## be surprised if for some names they return different results occ_data(scientificName = 'Ursus americanus', curlopts=list(verbose=TRUE)) key <- name_backbone(name = 'Ursus americanus', rank='species')$usageKey occ_data(taxonKey = key) # Search by dataset key occ_data(datasetKey='7b5d6a48-f762-11e1-a439-00145eb45e9a', limit=10) # Search by catalog number occ_data(catalogNumber="49366", limit=10) ## separate requests: use a vector of strings occ_data(catalogNumber=c("49366","Bird.27847588"), limit=10) ## one request, many instances of same parameter: use semi-colon sep. string occ_data(catalogNumber="49366;Bird.27847588", limit=10) # Use paging parameters (limit and start) to page. Note the different results # for the two queries below. occ_data(datasetKey='7b5d6a48-f762-11e1-a439-00145eb45e9a',start=10,limit=5) occ_data(datasetKey='7b5d6a48-f762-11e1-a439-00145eb45e9a',start=20,limit=5) # Many dataset keys ## separate requests: use a vector of strings occ_data(datasetKey=c("50c9509d-22c7-4a22-a47d-8c48425ef4a7", "7b5d6a48-f762-11e1-a439-00145eb45e9a"), limit=20) ## one request, many instances of same parameter: use semi-colon sep. string v="50c9509d-22c7-4a22-a47d-8c48425ef4a7;7b5d6a48-f762-11e1-a439-00145eb45e9a" occ_data(datasetKey = v, limit=20) # Search by recorder occ_data(recordedBy="smith", limit=20) # Many collector names ## separate requests: use a vector of strings occ_data(recordedBy=c("smith","BJ Stacey"), limit=10) ## one request, many instances of same parameter: use semi-colon sep. string occ_data(recordedBy="smith;BJ Stacey", limit=10) # recordedByID occ_data(recordedByID="https://orcid.org/0000-0003-1691-239X", limit=20) ## many at once ### separate searches ids <- c("https://orcid.org/0000-0003-1691-239X", "https://orcid.org/0000-0001-7569-1828", "https://orcid.org/0000-0002-0596-5376") res <- occ_data(recordedByID=ids, limit=20) res[[1]]$data$recordedByIDs[[1]] res[[2]]$data$recordedByIDs[[1]] res[[3]]$data$recordedByIDs[[1]] ### all in one search res <- occ_data(recordedByID=paste0(ids, collapse=";"), limit=20) unique(vapply(res$data$recordedByIDs, "[[", "", "value")) # identifiedByID occ_data(identifiedByID="https://orcid.org/0000-0003-4710-2648", limit=20) # Pass in curl options for extra fun occ_data(taxonKey=2433407, limit=20, curlopts=list(verbose=TRUE)) occ_data(taxonKey=2433407, limit=20, curlopts = list( noprogress = FALSE, progressfunction = function(down, up) { cat(sprintf("up: %d | down %d\n", up, down)) return(TRUE) } ) ) # occ_data(taxonKey=2433407, limit=20, curlopts=list(timeout_ms=1)) # Search for many species splist <- c('Cyanocitta stelleri', 'Junco hyemalis', 'Aix sponsa') keys <- sapply(splist, function(x) name_suggest(x)$data$key[1], USE.NAMES=FALSE) ## separate requests: use a vector of strings occ_data(taxonKey = keys, limit=5) ## one request, many instances of same parameter: use semi-colon sep. string occ_data(taxonKey = paste0(keys, collapse = ";"), limit=5) # Search using a synonym name # Note that you'll see a message printing out that the accepted name will # be used occ_data(scientificName = 'Pulsatilla patens', limit=5) # Search on latitidue and longitude occ_data(decimalLatitude=40, decimalLongitude=-120, limit = 10) # Search on a bounding box ## in well known text format ### polygon occ_data(geometry='POLYGON((30.1 10.1,40 40,20 40,10 20,30.1 10.1))', limit=20) ### multipolygon wkt <- 'MULTIPOLYGON(((-123 38,-116 38,-116 43,-123 43,-123 38)), ((-97 41,-93 41,-93 45,-97 45,-97 41)))' occ_data(geometry = gsub("\n\\s+", "", wkt), limit = 20) ### polygon and taxonkey key <- name_suggest(q='Aesculus hippocastanum')$data$key[1] occ_data(taxonKey=key, geometry='POLYGON((30.1 10.1,40 40,20 40,10 20,30.1 10.1))', limit=20) ## or using bounding box, converted to WKT internally occ_data(geometry=c(-125.0,38.4,-121.8,40.9), limit=20) ## you can seaerch on many geometry objects ### separate requests: use a vector of strings wkts <- c('POLYGON((-102.2 46,-102.2 43.7,-93.9 43.7,-93.9 46,-102.2 46))', 'POLYGON((30.1 10.1,40 40,20 40,10 20,30.1 10.1))') occ_data(geometry = wkts, limit=20) ### one request, many instances of same parameter: use semi-colon sep. string occ_data(geometry = paste0(wkts, collapse = ";"), limit=20) # Search on a long WKT string - too long for a GBIF search API request ## By default, a very long WKT string will likely cause a request failure as ## GBIF only handles strings up to about 1500 characters long. You can leave as is, or ## - Alternatively, you can choose to break up your polygon into many, and do a ## data request on each piece, and the output is put back together (see below) ## - Or, 2nd alternatively, you could use the GBIF download API wkt <- [2241 chars quoted with '"'] wkt <- gsub("\n", " ", wkt) #### Default option with large WKT string fails # res <- occ_data(geometry = wkt) #### if WKT too long, with 'geom_big=bbox': makes into bounding box res <- occ_data(geometry = wkt, geom_big = "bbox") library("rgeos") library("sp") wktsp <- readWKT(wkt) plot(wktsp) coordinates(res$data) <- ~decimalLongitude+decimalLatitude points(res$data) #### Or, use 'geom_big=axe' (res <- occ_data(geometry = wkt, geom_big = "axe")) ##### manipulate essentially number of polygons that result, so number of requests ###### default geom_size is 40 ###### fewer calls (res <- occ_data(geometry = wkt, geom_big = "axe", geom_size=50)) ###### more calls (res <- occ_data(geometry = wkt, geom_big = "axe", geom_size=30)) # Search on country occ_data(country='US', limit=20) isocodes[grep("France", isocodes$name),"code"] occ_data(country='FR', limit=20) occ_data(country='DE', limit=20) ### separate requests: use a vector of strings occ_data(country=c('US','DE'), limit=20) ### one request, many instances of same parameter: use semi-colon sep. string occ_data(country = 'US;DE', limit=20) # Get only occurrences with lat/long data occ_data(taxonKey=key, hasCoordinate=TRUE, limit=20) # Get only occurrences that were recorded as living specimens occ_data(basisOfRecord="LIVING_SPECIMEN", hasCoordinate=TRUE, limit=20) # Get occurrences for a particular eventDate occ_data(taxonKey=key, eventDate="2013", limit=20) occ_data(taxonKey=key, year="2013", limit=20) occ_data(taxonKey=key, month="6", limit=20) # Get occurrences based on depth key <- name_backbone(name='Salmo salar', kingdom='animals')$speciesKey occ_data(taxonKey=key, depth=1, limit=20) # Get occurrences based on elevation key <- name_backbone(name='Puma concolor', kingdom='animals')$speciesKey occ_data(taxonKey=key, elevation=50, hasCoordinate=TRUE, limit=20) # Get occurrences based on institutionCode occ_data(institutionCode="TLMF", limit=20) ### separate requests: use a vector of strings occ_data(institutionCode=c("TLMF","ArtDatabanken"), limit=20) ### one request, many instances of same parameter: use semi-colon sep. string occ_data(institutionCode = "TLMF;ArtDatabanken", limit=20) # Get occurrences based on collectionCode occ_data(collectionCode="Floristic Databases MV - Higher Plants", limit=20) ### separate requests: use a vector of strings occ_data(collectionCode=c("Floristic Databases MV - Higher Plants", "Artport"), limit = 20) ### one request, many instances of same parameter: use semi-colon sep. string occ_data(collectionCode = "Floristic Databases MV - Higher Plants;Artport", limit = 20) # Get only those occurrences with spatial issues occ_data(taxonKey=key, hasGeospatialIssue=TRUE, limit=20) # Search using a query string occ_data(search="kingfisher", limit=20) # search on repatriated - doesn't work right now # occ_data(repatriated = "") # search on phylumKey occ_data(phylumKey = 7707728, limit = 5) # search on kingdomKey occ_data(kingdomKey = 1, limit = 5) # search on classKey occ_data(classKey = 216, limit = 5) # search on orderKey occ_data(orderKey = 7192402, limit = 5) # search on familyKey occ_data(familyKey = 3925, limit = 5) # search on genusKey occ_data(genusKey = 1935496, limit = 5) # search on establishmentMeans occ_data(establishmentMeans = "INVASIVE", limit = 5) occ_data(establishmentMeans = "NATIVE", limit = 5) occ_data(establishmentMeans = "UNCERTAIN", limit = 5) ### separate requests: use a vector of strings occ_data(establishmentMeans = c("INVASIVE", "NATIVE"), limit = 5) ### one request, many instances of same parameter: use semi-colon sep. string occ_data(establishmentMeans = "INVASIVE;NATIVE", limit = 5) # search on protocol occ_data(protocol = "DIGIR", limit = 5) # search on license occ_data(license = "CC_BY_4_0", limit = 5) # search on organismId occ_data(organismId = "100", limit = 5) # search on publishingOrg occ_data(publishingOrg = "28eb1a3f-1c15-4a95-931a-4af90ecb574d", limit = 5) # search on stateProvince occ_data(stateProvince = "California", limit = 5) # search on waterBody occ_data(waterBody = "pacific ocean", limit = 5) # search on locality occ_data(locality = "Trondheim", limit = 5) ### separate requests: use a vector of strings res <- occ_data(locality = c("Trondheim", "Hovekilen"), limit = 5) res$Trondheim$data res$Hovekilen$data ### one request, many instances of same parameter: use semi-colon sep. string occ_data(locality = "Trondheim;Hovekilen", limit = 5) # Range queries ## See Detail for parameters that support range queries occ_data(depth='50,100', limit = 20) ### this is not a range search, but does two searches for each depth occ_data(depth=c(50,100), limit = 20) ## Range search with year occ_data(year='1999,2000', limit=20) ## Range search with latitude occ_data(decimalLatitude='29.59,29.6', limit = 20) # Search by specimen type status ## Look for possible values of the typeStatus parameter looking at the typestatus dataset occ_data(typeStatus = 'allotype', limit = 20)$data[,c('name','typeStatus')] # Search by specimen record number ## This is the record number of the person/group that submitted the data, not GBIF's numbers ## You can see that many different groups have record number 1, so not super helpful occ_data(recordNumber = 1, limit = 20)$data[,c('name','recordNumber','recordedBy')] # Search by last time interpreted: Date the record was last modified in GBIF ## The lastInterpreted parameter accepts ISO 8601 format dates, including ## yyyy, yyyy-MM, yyyy-MM-dd, or MM-dd. Range queries are accepted for lastInterpreted occ_data(lastInterpreted = '2016-04-02', limit = 20) # Search for occurrences with images occ_data(mediaType = 'StillImage', limit = 20) occ_data(mediaType = 'MovingImage', limit = 20) occ_data(mediaType = 'Sound', limit = 20) # Search by continent ## One of africa, antarctica, asia, europe, north_america, oceania, or ## south_america occ_data(continent = 'south_america', limit = 20)$meta occ_data(continent = 'africa', limit = 20)$meta occ_data(continent = 'oceania', limit = 20)$meta occ_data(continent = 'antarctica', limit = 20)$meta ### separate requests: use a vector of strings occ_data(continent = c('south_america', 'oceania'), limit = 20) ### one request, many instances of same parameter: use semi-colon sep. string occ_data(continent = 'south_america;oceania', limit = 20) # Query based on issues - see Details for options ## one issue x <- occ_data(taxonKey=1, issue='DEPTH_UNLIKELY', limit = 20) x$data[,c('name','key','decimalLatitude','decimalLongitude','depth')] ## two issues occ_data(taxonKey=1, issue=c('DEPTH_UNLIKELY','COORDINATE_ROUNDED'), limit = 20) # Show all records in the Arizona State Lichen Collection that cant be matched to the GBIF # backbone properly: occ_data(datasetKey='84c0e1a0-f762-11e1-a439-00145eb45e9a', issue=c('TAXON_MATCH_NONE','TAXON_MATCH_HIGHERRANK'), limit = 20) # Parsing output by issue (res <- occ_data(geometry='POLYGON((30.1 10.1,40 40,20 40,10 20,30.1 10.1))', limit = 50)) ## what do issues mean, can print whole table, or search for matches head(gbif_issues()) gbif_issues()[ gbif_issues()$code %in% c('cdround','cudc','gass84','txmathi'), ] ## or parse issues in various ways ### remove data rows with certain issue classes library('magrittr') res %>% occ_issues(gass84) ### split issues into separate columns res %>% occ_issues(mutate = "split") ### expand issues to more descriptive names res %>% occ_issues(mutate = "expand") ### split and expand res %>% occ_issues(mutate = "split_expand") ### split, expand, and remove an issue class res %>% occ_issues(-cudc, mutate = "split_expand") }