First load the package:
The occurrence() function provides access to raw occurrence data. For example, to fetch all occurrences by scientific name:
occurrence("Abra aequalis")
#> # A tibble: 767 x 99
#> country date_year scientificNameID year scientificName superfamilyid
#> <chr> <int> <chr> <chr> <chr> <int>
#> 1 United… 1976 urn:lsid:marine… 1976 Abra aequalis 14636
#> 2 United… 1977 urn:lsid:marine… 1977 Abra aequalis 14636
#> 3 <NA> 1986 urn:lsid:marine… 1986 Abra aequalis 14636
#> 4 <NA> 2013 urn:lsid:marine… <NA> Abra aequalis 14636
#> 5 <NA> 1994 urn:lsid:marine… <NA> Abra aequalis 14636
#> 6 <NA> 1972 urn:lsid:marine… <NA> Abra aequalis 14636
#> 7 <NA> 1996 urn:lsid:marine… <NA> Abra aequalis 14636
#> 8 Colomb… 2001 urn:lsid:marine… 2001 Abra aequalis 14636
#> 9 <NA> 2013 urn:lsid:marine… <NA> Abra aequalis 14636
#> 10 <NA> 2001 urn:lsid:marine… <NA> Abra aequalis 14636
#> # … with 757 more rows, and 93 more variables: individualCount <chr>,
#> # dropped <lgl>, aphiaID <int>, decimalLatitude <dbl>, subclassid <int>,
#> # phylumid <int>, familyid <int>, basisOfRecord <chr>, subterclassid <int>,
#> # maximumDepthInMeters <dbl>, id <chr>, day <chr>, order <chr>,
#> # dataset_id <chr>, decimalLongitude <dbl>, collectionCode <chr>,
#> # date_end <dbl>, speciesid <int>, superfamily <chr>, date_start <dbl>,
#> # month <chr>, genus <chr>, bibliographicCitation <chr>, subterclass <chr>,
#> # eventDate <chr>, superorder <chr>, coordinateUncertaintyInMeters <chr>,
#> # absence <lgl>, superorderid <int>, genusid <int>,
#> # originalScientificName <chr>, marine <lgl>, minimumDepthInMeters <dbl>,
#> # infraclassid <int>, institutionCode <chr>, date_mid <dbl>,
#> # infraclass <chr>, class <chr>, orderid <int>, waterBody <chr>,
#> # kingdom <chr>, recordedBy <chr>, classid <int>, phylum <chr>,
#> # species <chr>, subclass <chr>, family <chr>, kingdomid <int>,
#> # node_id <chr>, flags <chr>, sss <dbl>, shoredistance <int>, sst <dbl>,
#> # bathymetry <int>, dynamicProperties <chr>, catalogNumber <chr>,
#> # locality <chr>, scientificNameAuthorship <chr>, identifiedBy <chr>,
#> # depth <dbl>, type <chr>, taxonRemarks <chr>, occurrenceStatus <chr>,
#> # materialSampleID <chr>, occurrenceID <chr>, ownerInstitutionCode <chr>,
#> # samplingProtocol <chr>, taxonRank <chr>, datasetName <chr>,
#> # datasetID <chr>, collectionID <chr>, eventID <chr>, habitat <chr>,
#> # associatedMedia <lgl>, associatedSequences <lgl>, county <chr>,
#> # coordinatePrecision <chr>, associatedReferences <chr>, fieldNumber <chr>,
#> # stateProvince <chr>, preparations <chr>, occurrenceRemarks <chr>,
#> # verbatimDepth <chr>, modified <lgl>, infraspecificEpithet <lgl>,
#> # recordNumber <lgl>, higherGeography <chr>, continent <chr>,
#> # typeStatus <lgl>, geodeticDatum <lgl>, specificEpithet <chr>,
#> # georeferenceSources <lgl>, dateIdentified <chr>Alternatively, occurrences can be fetched by AphiaID:
occurrence(taxonid = 293683)
#> # A tibble: 767 x 99
#> country date_year scientificNameID year scientificName superfamilyid
#> <chr> <int> <chr> <chr> <chr> <int>
#> 1 United… 1976 urn:lsid:marine… 1976 Abra aequalis 14636
#> 2 United… 1977 urn:lsid:marine… 1977 Abra aequalis 14636
#> 3 <NA> 1986 urn:lsid:marine… 1986 Abra aequalis 14636
#> 4 <NA> 2013 urn:lsid:marine… <NA> Abra aequalis 14636
#> 5 <NA> 1994 urn:lsid:marine… <NA> Abra aequalis 14636
#> 6 <NA> 1972 urn:lsid:marine… <NA> Abra aequalis 14636
#> 7 <NA> 1996 urn:lsid:marine… <NA> Abra aequalis 14636
#> 8 Colomb… 2001 urn:lsid:marine… 2001 Abra aequalis 14636
#> 9 <NA> 2013 urn:lsid:marine… <NA> Abra aequalis 14636
#> 10 <NA> 2001 urn:lsid:marine… <NA> Abra aequalis 14636
#> # … with 757 more rows, and 93 more variables: individualCount <chr>,
#> # dropped <lgl>, aphiaID <int>, decimalLatitude <dbl>, subclassid <int>,
#> # phylumid <int>, familyid <int>, basisOfRecord <chr>, subterclassid <int>,
#> # maximumDepthInMeters <dbl>, id <chr>, day <chr>, order <chr>,
#> # dataset_id <chr>, decimalLongitude <dbl>, collectionCode <chr>,
#> # date_end <dbl>, speciesid <int>, superfamily <chr>, date_start <dbl>,
#> # month <chr>, genus <chr>, bibliographicCitation <chr>, subterclass <chr>,
#> # eventDate <chr>, superorder <chr>, coordinateUncertaintyInMeters <chr>,
#> # absence <lgl>, superorderid <int>, genusid <int>,
#> # originalScientificName <chr>, marine <lgl>, minimumDepthInMeters <dbl>,
#> # infraclassid <int>, institutionCode <chr>, date_mid <dbl>,
#> # infraclass <chr>, class <chr>, orderid <int>, waterBody <chr>,
#> # kingdom <chr>, recordedBy <chr>, classid <int>, phylum <chr>,
#> # species <chr>, subclass <chr>, family <chr>, kingdomid <int>,
#> # node_id <chr>, flags <chr>, sss <dbl>, shoredistance <int>, sst <dbl>,
#> # bathymetry <int>, dynamicProperties <chr>, catalogNumber <chr>,
#> # locality <chr>, scientificNameAuthorship <chr>, identifiedBy <chr>,
#> # depth <dbl>, type <chr>, taxonRemarks <chr>, occurrenceStatus <chr>,
#> # materialSampleID <chr>, occurrenceID <chr>, ownerInstitutionCode <chr>,
#> # samplingProtocol <chr>, taxonRank <chr>, datasetName <chr>,
#> # datasetID <chr>, collectionID <chr>, eventID <chr>, habitat <chr>,
#> # associatedMedia <lgl>, associatedSequences <lgl>, county <chr>,
#> # coordinatePrecision <chr>, associatedReferences <chr>, fieldNumber <chr>,
#> # stateProvince <chr>, preparations <chr>, occurrenceRemarks <chr>,
#> # verbatimDepth <chr>, modified <lgl>, infraspecificEpithet <lgl>,
#> # recordNumber <lgl>, higherGeography <chr>, continent <chr>,
#> # typeStatus <lgl>, geodeticDatum <lgl>, specificEpithet <chr>,
#> # georeferenceSources <lgl>, dateIdentified <chr>Other parameters include geometry, which accepts polygons in WKT format:
occurrence("Abra alba", geometry = "POLYGON ((2.59689 51.16772, 2.62436 51.14059, 2.76066 51.19225, 2.73216 51.20946, 2.59689 51.16772))")
#> # A tibble: 319 x 85
#> date_year scientificNameID year scientificName superfamilyid individualCount
#> <int> <chr> <chr> <chr> <int> <chr>
#> 1 1984 urn:lsid:marine… 1984 Abra alba 14636 23.0
#> 2 2009 urn:lsid:marine… 2009 Abra alba 14636 <NA>
#> 3 2002 urn:lsid:marine… 2002 Abra alba 14636 <NA>
#> 4 2002 urn:lsid:marine… 2002 Abra alba 14636 <NA>
#> 5 1995 urn:lsid:marine… 1995 Abra alba 14636 78.0
#> 6 2012 urn:lsid:marine… 2012 Abra alba 14636 <NA>
#> 7 1999 urn:lsid:marine… 1999 Abra alba 14636 15.0
#> 8 2010 urn:lsid:marine… 2010 Abra alba 14636 <NA>
#> 9 1989 urn:lsid:marine… 1989 Abra alba 14636 47.0
#> 10 1991 urn:lsid:marine… 1991 Abra alba 14636 103.0
#> # … with 309 more rows, and 79 more variables: dropped <lgl>,
#> # fieldNumber <chr>, aphiaID <int>, decimalLatitude <dbl>, subclassid <int>,
#> # phylumid <int>, familyid <int>, catalogNumber <chr>,
#> # occurrenceStatus <chr>, basisOfRecord <chr>, subterclassid <int>,
#> # modified <chr>, maximumDepthInMeters <dbl>, id <chr>, day <chr>,
#> # order <chr>, dataset_id <chr>, locality <chr>, decimalLongitude <dbl>,
#> # collectionCode <chr>, date_end <dbl>, speciesid <int>, occurrenceID <chr>,
#> # superfamily <chr>, date_start <dbl>, month <chr>, genus <chr>,
#> # samplingProtocol <chr>, subterclass <chr>, eventDate <chr>, eventID <chr>,
#> # superorder <chr>, absence <lgl>, samplingEffort <chr>, superorderid <int>,
#> # genusid <int>, originalScientificName <chr>, marine <lgl>,
#> # minimumDepthInMeters <dbl>, infraclassid <int>, institutionCode <chr>,
#> # date_mid <dbl>, infraclass <chr>, class <chr>, orderid <int>, sex <chr>,
#> # geodeticDatum <chr>, kingdom <chr>, recordedBy <chr>, classid <int>,
#> # phylum <chr>, lifeStage <chr>, species <chr>, subclass <chr>,
#> # datasetID <chr>, family <chr>, kingdomid <int>, node_id <chr>, flags <chr>,
#> # sss <dbl>, shoredistance <int>, sst <dbl>, bathymetry <dbl>,
#> # language <chr>, footprintSRS <chr>, datasetName <chr>, country <chr>,
#> # references <chr>, dynamicProperties <chr>, bibliographicCitation <chr>,
#> # continent <chr>, scientificNameAuthorship <chr>, specificEpithet <chr>,
#> # verbatimDepth <chr>, occurrenceRemarks <chr>, footprintWKT <chr>,
#> # locationID <chr>, depth <dbl>, eventTime <chr>A convenience function map_leaflet() is provided to visualize occurrences on an interactive map:
map_leaflet(occurrence("Abra sibogai"))The checklist() function returns all taxa observed for a given set of filters.
checklist("Semelidae")
#> # A tibble: 108 x 38
#> scientificName scientificNameA… taxonID taxonRank taxonomicStatus
#> <chr> <chr> <int> <chr> <chr>
#> 1 Abra alba (W. Wood, 1802) 141433 Species accepted
#> 2 Abra nitida (O. F. Müller, … 141435 Species accepted
#> 3 Scrobicularia… (da Costa, 1778) 141424 Species accepted
#> 4 Abra prismati… (Montagu, 1808) 141436 Species accepted
#> 5 Abra tenuis (Montagu, 1803) 141439 Species accepted
#> 6 Abra Lamarck, 1818 138474 Genus accepted
#> 7 Theora lubrica Gould, 1861 233903 Species accepted
#> 8 Semelidae Stoliczka, 1870… 1781 Family accepted
#> 9 Abra aequalis (Say, 1822) 293683 Species accepted
#> 10 Abra segmentum (Récluz, 1843) 141438 Species accepted
#> # … with 98 more rows, and 33 more variables: acceptedNameUsage <chr>,
#> # acceptedNameUsageID <int>, is_marine <lgl>, is_brackish <lgl>,
#> # kingdom <chr>, phylum <chr>, class <chr>, subclass <chr>, infraclass <chr>,
#> # subterclass <chr>, superorder <chr>, order <chr>, superfamily <chr>,
#> # family <chr>, kingdomid <int>, phylumid <int>, classid <int>,
#> # subclassid <int>, infraclassid <int>, subterclassid <int>,
#> # superorderid <int>, orderid <int>, superfamilyid <int>, familyid <int>,
#> # records <int>, genus <chr>, genusid <int>, species <chr>, speciesid <int>,
#> # is_freshwater <lgl>, is_terrestrial <lgl>, subspecies <chr>,
#> # subspeciesid <int>Just like the occurrence() function, checklist() accepts WKT geometries:
checklist(geometry = "POLYGON ((2.59689 51.16772, 2.62436 51.14059, 2.76066 51.19225, 2.73216 51.20946, 2.59689 51.16772))")
#> # A tibble: 901 x 69
#> scientificName taxonID taxonomicStatus acceptedNameUsa… acceptedNameUsa…
#> <chr> <int> <chr> <chr> <int>
#> 1 Nematoda 799 accepted Nematoda 799
#> 2 Abra alba 141433 accepted Abra alba 141433
#> 3 Sabatieria ce… 121360 accepted Sabatieria celt… 121360
#> 4 Sabatieria pu… 153130 accepted Sabatieria punc… 153130
#> 5 Spiophanes bo… 131187 accepted Spiophanes bomb… 131187
#> 6 Kurtiella bid… 345281 accepted Kurtiella biden… 345281
#> 7 Nephtys hombe… 130359 accepted Nephtys homberg… 130359
#> 8 Oligochaeta 2036 accepted Oligochaeta 2036
#> 9 Cirratulidae 919 accepted Cirratulidae 919
#> 10 Fabulina fabu… 146907 accepted Fabulina fabula 146907
#> # … with 891 more rows, and 64 more variables: is_marine <lgl>,
#> # is_brackish <lgl>, is_freshwater <lgl>, is_terrestrial <lgl>,
#> # records <int>, taxonRank <chr>, kingdom <chr>, kingdomid <int>,
#> # phylum <chr>, phylumid <int>, scientificNameAuthorship <chr>, class <chr>,
#> # classid <int>, subclass <chr>, order <chr>, superfamily <chr>,
#> # family <chr>, subclassid <int>, orderid <int>, superfamilyid <int>,
#> # familyid <int>, infraclass <chr>, infraclassid <int>, subterclass <chr>,
#> # superorder <chr>, subterclassid <int>, superorderid <int>, suborder <chr>,
#> # suborderid <int>, subfamily <chr>, subfamilyid <int>, subphylum <chr>,
#> # subphylumid <int>, superclass <chr>, superclassid <int>, subkingdom <chr>,
#> # infrakingdom <chr>, subkingdomid <int>, infrakingdomid <int>, genus <chr>,
#> # genusid <int>, infraphylum <chr>, infraphylumid <int>, species <chr>,
#> # speciesid <int>, infraorder <chr>, parvorder <chr>, infraorderid <int>,
#> # parvorderid <int>, tribe <chr>, tribeid <int>, subgenus <chr>,
#> # subgenusid <int>, category <chr>, section <chr>, subsection <chr>,
#> # sectionid <int>, subsectionid <int>, subspecies <chr>, subspeciesid <int>,
#> # variety <chr>, varietyid <int>, forma <chr>, formaid <int>The package also provides access to MeasurementOrFact records associated with occurrences. When calling occurrence(), MeasurementOrFact records can be included by setting mof = true.
occ <- occurrence("Abra tenuis", mof = TRUE)MeasurementOrFact records are nested in the occurrence, but the measurements() function allows you to extract them to a flat data frame. Use the fields parameter to indicate which occurrence fields need to be preserved in the measurements table.
mof <- measurements(occ, fields = c("scientificName", "decimalLongitude", "decimalLatitude"))
mof
#> # A tibble: 19,355 x 17
#> scientificName decimalLongitude decimalLatitude measurementDete…
#> <chr> <dbl> <dbl> <chr>
#> 1 Abra tenuis -1.20 46.3 <NA>
#> 2 Abra tenuis -1.20 46.3 <NA>
#> 3 Abra tenuis -1.20 46.3 <NA>
#> 4 Abra tenuis -1.20 46.3 <NA>
#> 5 Abra tenuis -1.20 46.3 <NA>
#> 6 Abra tenuis -1.18 46.3 <NA>
#> 7 Abra tenuis -1.18 46.3 <NA>
#> 8 Abra tenuis -1.18 46.3 <NA>
#> 9 Abra tenuis -1.18 46.3 <NA>
#> 10 Abra tenuis -1.18 46.3 <NA>
#> # … with 19,345 more rows, and 13 more variables: measurementAccuracy <chr>,
#> # measurementValue <chr>, measurementRemarks <chr>, measurementValueID <chr>,
#> # level <int>, occurrenceID <chr>, measurementUnit <chr>,
#> # measurementDeterminedDate <chr>, measurementType <chr>,
#> # measurementUnitID <chr>, measurementTypeID <chr>, measurementID <chr>,
#> # measurementMethod <chr>Note that the MeasurementOrFact fields can be used as parameters to the occurrence() function. For example, to only get occurrences with associated biomass measurements:
library(dplyr)
occurrence("Abra tenuis", mof = TRUE, measurementtype = "biomass") %>%
measurements()
#> # A tibble: 29 x 15
#> id measurementDete… measurementAccu… measurementValue measurementRema…
#> <chr> <lgl> <lgl> <chr> <lgl>
#> 1 1878… NA NA mire NA
#> 2 1878… NA NA 0,12 NA
#> 3 267d… NA NA mire NA
#> 4 267d… NA NA 0,11 NA
#> 5 26d2… NA NA mire NA
#> 6 26d2… NA NA 0,25 NA
#> 7 2924… NA NA mire, putrido NA
#> 8 2924… NA NA 0,05 NA
#> 9 3534… NA NA mire with debris NA
#> 10 3534… NA NA 0,05 NA
#> # … with 19 more rows, and 10 more variables: measurementValueID <lgl>,
#> # level <int>, occurrenceID <chr>, measurementUnit <chr>,
#> # measurementDeterminedDate <lgl>, measurementType <chr>,
#> # measurementUnitID <chr>, measurementTypeID <lgl>, measurementID <lgl>,
#> # measurementMethod <lgl>