Getting started with the robis package

This package is a client for the OBIS API. It includes functions for data access, as well as a few helper functions for visualizing occurrence data and extracting nested MeasurementOrFact or DNADerivedData records.

Occurrence data

The occurrence() function provides access to raw occurrence data. For example, to fetch all occurrences by scientific name:

occ <- occurrence("Abra aequalis")
occ
#> # A tibble: 767 x 103
#>    date_year scientificNameID          year  scientificName dynamicProperties   
#>        <int> <chr>                     <chr> <chr>          <chr>               
#>  1      1986 urn:lsid:marinespecies.o… 1986  Abra aequalis  temperature=10;     
#>  2      1976 urn:lsid:marinespecies.o… 1976  Abra aequalis  <NA>                
#>  3      1992 urn:lsid:marinespecies.o… 1992  Abra aequalis  <NA>                
#>  4      2015 urn:lsid:marinespecies.o… <NA>  Abra aequalis  <NA>                
#>  5      2002 urn:lsid:marinespecies.o… 2002  Abra aequalis  <NA>                
#>  6      2013 urn:lsid:marinespecies.o… <NA>  Abra aequalis  <NA>                
#>  7      2014 urn:lsid:marinespecies.o… <NA>  Abra aequalis  <NA>                
#>  8      1978 urn:lsid:marinespecies.o… 1978  Abra aequalis  observedindividualc…
#>  9      1994 urn:lsid:marinespecies.o… <NA>  Abra aequalis  <NA>                
#> 10      2013 urn:lsid:marinespecies.o… <NA>  Abra aequalis  <NA>                
#> # … with 757 more rows, and 98 more variables: superfamilyid <int>,
#> #   individualCount <chr>, dropped <lgl>, aphiaID <int>, decimalLatitude <dbl>,
#> #   subclassid <int>, phylumid <int>, familyid <int>, catalogNumber <chr>,
#> #   basisOfRecord <chr>, subterclassid <int>, maximumDepthInMeters <dbl>,
#> #   id <chr>, order <chr>, dataset_id <chr>, locality <chr>,
#> #   decimalLongitude <dbl>, collectionCode <chr>, date_end <dbl>,
#> #   speciesid <int>, superfamily <chr>, date_start <dbl>, genus <chr>,
#> #   subterclass <chr>, eventDate <chr>, superorder <chr>,
#> #   scientificNameAuthorship <chr>, absence <lgl>, superorderid <int>,
#> #   genusid <int>, originalScientificName <chr>, marine <lgl>,
#> #   minimumDepthInMeters <dbl>, infraclassid <int>, institutionCode <chr>,
#> #   date_mid <dbl>, infraclass <chr>, identifiedBy <chr>, class <chr>,
#> #   orderid <int>, kingdom <chr>, classid <int>, phylum <chr>, species <chr>,
#> #   subclass <chr>, family <chr>, kingdomid <int>, node_id <chr>, flags <chr>,
#> #   sss <dbl>, depth <dbl>, shoredistance <int>, sst <dbl>, bathymetry <int>,
#> #   country <chr>, day <chr>, month <chr>, bibliographicCitation <chr>,
#> #   waterBody <chr>, recordedBy <chr>, coordinatePrecision <chr>, type <chr>,
#> #   taxonRemarks <chr>, occurrenceStatus <chr>, materialSampleID <chr>,
#> #   occurrenceID <chr>, ownerInstitutionCode <chr>, samplingProtocol <chr>,
#> #   taxonRank <chr>, datasetName <chr>, datasetID <chr>,
#> #   associatedReferences <chr>, fieldNumber <chr>, stateProvince <chr>,
#> #   preparations <chr>, county <chr>, modified <lgl>,
#> #   infraspecificEpithet <lgl>, recordNumber <chr>, higherGeography <chr>,
#> #   continent <chr>, typeStatus <lgl>, geodeticDatum <lgl>,
#> #   specificEpithet <chr>, georeferenceSources <lgl>, verbatimDepth <chr>,
#> #   occurrenceRemarks <chr>, collectionID <chr>, eventID <chr>,
#> #   dateIdentified <chr>, habitat <chr>, institutionID <chr>, organismID <chr>,
#> #   eventRemarks <chr>, taxonID <chr>, locationRemarks <chr>,
#> #   countryCode <chr>, locationID <chr>

ggplot(occ) +
  geom_bar(aes(date_year), stat = "count", width = 1)
#> Warning: Removed 25 rows containing non-finite values (stat_count).

Alternatively, occurrences can be fetched by AphiaID:

occurrence(taxonid = 293683)
#> # A tibble: 767 x 103
#>    date_year scientificNameID          year  scientificName dynamicProperties   
#>        <int> <chr>                     <chr> <chr>          <chr>               
#>  1      1986 urn:lsid:marinespecies.o… 1986  Abra aequalis  temperature=10;     
#>  2      1976 urn:lsid:marinespecies.o… 1976  Abra aequalis  <NA>                
#>  3      1992 urn:lsid:marinespecies.o… 1992  Abra aequalis  <NA>                
#>  4      2015 urn:lsid:marinespecies.o… <NA>  Abra aequalis  <NA>                
#>  5      2002 urn:lsid:marinespecies.o… 2002  Abra aequalis  <NA>                
#>  6      2013 urn:lsid:marinespecies.o… <NA>  Abra aequalis  <NA>                
#>  7      2014 urn:lsid:marinespecies.o… <NA>  Abra aequalis  <NA>                
#>  8      1978 urn:lsid:marinespecies.o… 1978  Abra aequalis  observedindividualc…
#>  9      1994 urn:lsid:marinespecies.o… <NA>  Abra aequalis  <NA>                
#> 10      2013 urn:lsid:marinespecies.o… <NA>  Abra aequalis  <NA>                
#> # … with 757 more rows, and 98 more variables: superfamilyid <int>,
#> #   individualCount <chr>, dropped <lgl>, aphiaID <int>, decimalLatitude <dbl>,
#> #   subclassid <int>, phylumid <int>, familyid <int>, catalogNumber <chr>,
#> #   basisOfRecord <chr>, subterclassid <int>, maximumDepthInMeters <dbl>,
#> #   id <chr>, order <chr>, dataset_id <chr>, locality <chr>,
#> #   decimalLongitude <dbl>, collectionCode <chr>, date_end <dbl>,
#> #   speciesid <int>, superfamily <chr>, date_start <dbl>, genus <chr>,
#> #   subterclass <chr>, eventDate <chr>, superorder <chr>,
#> #   scientificNameAuthorship <chr>, absence <lgl>, superorderid <int>,
#> #   genusid <int>, originalScientificName <chr>, marine <lgl>,
#> #   minimumDepthInMeters <dbl>, infraclassid <int>, institutionCode <chr>,
#> #   date_mid <dbl>, infraclass <chr>, identifiedBy <chr>, class <chr>,
#> #   orderid <int>, kingdom <chr>, classid <int>, phylum <chr>, species <chr>,
#> #   subclass <chr>, family <chr>, kingdomid <int>, node_id <chr>, flags <chr>,
#> #   sss <dbl>, depth <dbl>, shoredistance <int>, sst <dbl>, bathymetry <int>,
#> #   country <chr>, day <chr>, month <chr>, bibliographicCitation <chr>,
#> #   waterBody <chr>, recordedBy <chr>, coordinatePrecision <chr>, type <chr>,
#> #   taxonRemarks <chr>, occurrenceStatus <chr>, materialSampleID <chr>,
#> #   occurrenceID <chr>, ownerInstitutionCode <chr>, samplingProtocol <chr>,
#> #   taxonRank <chr>, datasetName <chr>, datasetID <chr>,
#> #   associatedReferences <chr>, fieldNumber <chr>, stateProvince <chr>,
#> #   preparations <chr>, county <chr>, modified <lgl>,
#> #   infraspecificEpithet <lgl>, recordNumber <chr>, higherGeography <chr>,
#> #   continent <chr>, typeStatus <lgl>, geodeticDatum <lgl>,
#> #   specificEpithet <chr>, georeferenceSources <lgl>, verbatimDepth <chr>,
#> #   occurrenceRemarks <chr>, collectionID <chr>, eventID <chr>,
#> #   dateIdentified <chr>, habitat <chr>, institutionID <chr>, organismID <chr>,
#> #   eventRemarks <chr>, taxonID <chr>, locationRemarks <chr>,
#> #   countryCode <chr>, locationID <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 dropped
#>        <int> <chr>                    <chr> <chr>                  <int> <lgl>  
#>  1      2016 urn:lsid:marinespecies.… 2016  Abra alba              14636 FALSE  
#>  2      2014 urn:lsid:marinespecies.… 2014  Abra alba              14636 FALSE  
#>  3      1995 urn:lsid:marinespecies.… 1995  Abra alba              14636 FALSE  
#>  4      2017 urn:lsid:marinespecies.… 2017  Abra alba              14636 FALSE  
#>  5      1995 urn:lsid:marinespecies.… 1995  Abra alba              14636 FALSE  
#>  6      2016 urn:lsid:marinespecies.… 2016  Abra alba              14636 FALSE  
#>  7      1997 urn:lsid:marinespecies.… 1997  Abra alba              14636 FALSE  
#>  8      1995 urn:lsid:marinespecies.… 1995  Abra alba              14636 FALSE  
#>  9      2009 urn:lsid:marinespecies.… 2009  Abra alba              14636 FALSE  
#> 10      2009 urn:lsid:marinespecies.… 2009  Abra alba              14636 FALSE  
#> # … with 309 more rows, and 79 more variables: fieldNumber <chr>,
#> #   aphiaID <int>, language <chr>, decimalLatitude <dbl>, subclassid <int>,
#> #   phylumid <int>, familyid <int>, catalogNumber <chr>,
#> #   occurrenceStatus <chr>, basisOfRecord <chr>, subterclassid <int>,
#> #   modified <chr>, 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>, footprintSRS <chr>, month <chr>, genus <chr>,
#> #   subterclass <chr>, eventDate <chr>, eventID <chr>, superorder <chr>,
#> #   absence <lgl>, superorderid <int>, genusid <int>,
#> #   originalScientificName <chr>, marine <lgl>, infraclassid <int>,
#> #   date_mid <dbl>, infraclass <chr>, class <chr>, orderid <int>,
#> #   datasetName <chr>, geodeticDatum <chr>, kingdom <chr>, recordedBy <chr>,
#> #   classid <int>, phylum <chr>, species <chr>, subclass <chr>,
#> #   datasetID <chr>, family <chr>, kingdomid <int>, node_id <chr>, flags <chr>,
#> #   sss <dbl>, shoredistance <int>, sst <dbl>, bathymetry <dbl>,
#> #   verbatimDepth <chr>, country <chr>, references <chr>,
#> #   dynamicProperties <chr>, individualCount <chr>,
#> #   bibliographicCitation <chr>, continent <chr>,
#> #   scientificNameAuthorship <chr>, samplingEffort <chr>,
#> #   institutionCode <chr>, specificEpithet <chr>, lifeStage <chr>,
#> #   samplingProtocol <chr>, sex <chr>, maximumDepthInMeters <dbl>,
#> #   minimumDepthInMeters <dbl>, footprintWKT <chr>, depth <dbl>,
#> #   locationID <chr>, waterBody <chr>, occurrenceRemarks <chr>

WKT strings can be created by drawing on a map using the get_geometry() function.

A convenience function map_leaflet() is provided to visualize occurrences on an interactive map:

map_leaflet(occurrence("Abra sibogai"))

Checklists

The checklist() function returns all taxa observed for a given set of filters.

cl <- checklist("Semelidae")
cl
#> # A tibble: 107 x 41
#>    scientificName   scientificNameAut… taxonID ncbi_id taxonRank taxonomicStatus
#>    <chr>            <chr>                <int>   <int> <chr>     <chr>          
#>  1 Abra alba        (W. Wood, 1802)     141433  399303 Species   accepted       
#>  2 Abra nitida      (O. F. Müller, 17…  141435  358434 Species   accepted       
#>  3 Scrobicularia p… (da Costa, 1778)    141424  665965 Species   accepted       
#>  4 Abra prismatica  (Montagu, 1808)     141436  183592 Species   accepted       
#>  5 Abra tenuis      (Montagu, 1803)     141439      NA Species   accepted       
#>  6 Abra             Lamarck, 1818       138474  121180 Genus     accepted       
#>  7 Abra segmentum   (Récluz, 1843)      141438      NA Species   accepted       
#>  8 Theora lubrica   Gould, 1861         233903 1230554 Species   accepted       
#>  9 Semelidae        Stoliczka, 1870 (…    1781  121179 Family    accepted       
#> 10 Abra aequalis    (Say, 1822)         293683 2175524 Species   accepted       
#> # … with 97 more rows, and 35 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>,
#> #   bold_id <int>, is_freshwater <lgl>, is_terrestrial <lgl>, wrims <lgl>,
#> #   subspecies <chr>, subspeciesid <int>

ggplot(cl %>% filter(!is.na(genus))) +
  geom_bar(aes(genus)) +
  coord_flip() +
  ylab("species count")

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: 902 x 73
#>    scientificName      taxonID ncbi_id taxonomicStatus acceptedNameUsage  
#>    <chr>                 <int>   <int> <chr>           <chr>              
#>  1 Nematoda                799    6231 accepted        Nematoda           
#>  2 Abra alba            141433  399303 accepted        Abra alba          
#>  3 Sabatieria celtica   121360  319964 accepted        Sabatieria celtica 
#>  4 Sabatieria punctata  153130  320140 accepted        Sabatieria punctata
#>  5 Spiophanes bombyx    131187  696728 accepted        Spiophanes bombyx  
#>  6 Kurtiella bidentata  345281 1177057 accepted        Kurtiella bidentata
#>  7 Nephtys hombergii    130359   36121 accepted        Nephtys hombergii  
#>  8 Oligochaeta            2036      NA accepted        Oligochaeta        
#>  9 Cirratulidae            919   46590 accepted        Cirratulidae       
#> 10 Fabulina fabula      146907      NA accepted        Fabulina fabula    
#> # … with 892 more rows, and 68 more variables: acceptedNameUsageID <int>,
#> #   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>, hab <lgl>, bold_id <int>,
#> #   species <chr>, speciesid <int>, infraorder <chr>, parvorder <chr>,
#> #   infraorderid <int>, parvorderid <int>, tribe <chr>, tribeid <int>,
#> #   wrims <lgl>, 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>

MeasurementOrFact records

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: 19,469 x 18
#>    id              scientificName decimalLongitude decimalLatitude measurementID
#>    <chr>           <chr>                     <dbl>           <dbl> <chr>        
#>  1 00037215-c2e8-… Abra tenuis               -1.22            45.9 <NA>         
#>  2 00037215-c2e8-… Abra tenuis               -1.22            45.9 <NA>         
#>  3 00037215-c2e8-… Abra tenuis               -1.22            45.9 <NA>         
#>  4 00037215-c2e8-… Abra tenuis               -1.22            45.9 <NA>         
#>  5 00037215-c2e8-… Abra tenuis               -1.22            45.9 <NA>         
#>  6 000e9ab4-bee5-… Abra tenuis               -1.20            46.3 <NA>         
#>  7 000e9ab4-bee5-… Abra tenuis               -1.20            46.3 <NA>         
#>  8 000e9ab4-bee5-… Abra tenuis               -1.20            46.3 <NA>         
#>  9 000e9ab4-bee5-… Abra tenuis               -1.20            46.3 <NA>         
#> 10 000e9ab4-bee5-… Abra tenuis               -1.20            46.3 <NA>         
#> # … with 19,459 more rows, and 13 more variables: occurrenceID <chr>,
#> #   measurementType <chr>, measurementTypeID <chr>, measurementValue <chr>,
#> #   measurementValueID <chr>, measurementAccuracy <chr>, measurementUnit <chr>,
#> #   measurementUnitID <chr>, measurementDeterminedDate <chr>,
#> #   measurementDeterminedBy <chr>, measurementMethod <chr>,
#> #   measurementRemarks <chr>, level <int>

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: 44 x 15
#>    id        measurementID occurrenceID     measurementType measurementTypeID   
#>    <chr>     <chr>         <chr>            <chr>           <chr>               
#>  1 08269691… <NA>          476637_urn:lsid… individualCount http://vocab.nerc.a…
#>  2 08269691… <NA>          476637_urn:lsid… biomass         <NA>                
#>  3 08269691… <NA>          <NA>             sediment type   http://vocab.nerc.a…
#>  4 16af269f… <NA>          475202_urn:lsid… individualCount http://vocab.nerc.a…
#>  5 16af269f… <NA>          475202_urn:lsid… biomass         <NA>                
#>  6 16af269f… <NA>          <NA>             sediment type   http://vocab.nerc.a…
#>  7 2a20624e… <NA>          475273_urn:lsid… individualCount http://vocab.nerc.a…
#>  8 2a20624e… <NA>          475273_urn:lsid… biomass         <NA>                
#>  9 2a20624e… <NA>          <NA>             sediment type   http://vocab.nerc.a…
#> 10 2b78df21… <NA>          475141_urn:lsid… individualCount http://vocab.nerc.a…
#> # … with 34 more rows, and 10 more variables: measurementValue <chr>,
#> #   measurementValueID <chr>, measurementAccuracy <chr>, measurementUnit <chr>,
#> #   measurementUnitID <chr>, measurementDeterminedDate <chr>,
#> #   measurementDeterminedBy <chr>, measurementMethod <chr>,
#> #   measurementRemarks <chr>, level <int>

DNADerivedData records

Just like MeasurementOrFact records, nested DNADerivedData records can be extracted from the occurrence results.

occ <- occurrence("Prymnesiophyceae", datasetid = "62b97724-da17-4ca7-9b26-b2a22aeaab51", dna = TRUE)
occ
#> # A tibble: 1,136 x 54
#>    eventID   date_year scientificNameID   scientificName absence dropped genusid
#>    <chr>         <int> <chr>              <chr>          <lgl>   <lgl>     <int>
#>  1 28215c01…      2015 urn:lsid:marinesp… Chrysochromul… FALSE   FALSE    115090
#>  2 24416c01…      2016 urn:lsid:marinesp… Phaeocystaceae FALSE   FALSE        NA
#>  3 34916c01…      2016 urn:lsid:marinesp… Prymnesiaceae  FALSE   FALSE        NA
#>  4 11216c01…      2016 urn:lsid:marinesp… Chrysochromul… FALSE   FALSE    115090
#>  5 34916c01…      2016 urn:lsid:marinesp… Chrysochromul… FALSE   FALSE    115090
#>  6 24416c01…      2016 urn:lsid:marinesp… Chrysochromul… FALSE   FALSE    115090
#>  7 30214c01…      2014 urn:lsid:marinesp… Chrysochromul… FALSE   FALSE    115090
#>  8 14213c01…      2013 urn:lsid:marinesp… Chrysochromul… FALSE   FALSE    115090
#>  9 CANON16c…      2016 urn:lsid:marinesp… Chrysochromul… FALSE   FALSE    115090
#> 10 22013c01…      2013 urn:lsid:marinesp… Chrysochromul… FALSE   FALSE    115090
#> # … with 1,126 more rows, and 47 more variables: aphiaID <int>,
#> #   decimalLatitude <dbl>, taxonID <chr>, originalScientificName <chr>,
#> #   marine <lgl>, phylumid <int>, familyid <int>, basisOfRecord <chr>,
#> #   taxonConceptID <chr>, subkingdom <chr>, date_mid <dbl>,
#> #   identificationRemarks <chr>, nameAccordingTo <chr>, id <chr>, class <chr>,
#> #   order <chr>, identificationReferences <chr>, organismQuantity <chr>,
#> #   sampleSizeUnit <chr>, orderid <int>, dataset_id <chr>,
#> #   decimalLongitude <dbl>, date_end <dbl>, speciesid <int>,
#> #   occurrenceID <chr>, kingdom <chr>, subkingdomid <int>, date_start <dbl>,
#> #   classid <int>, phylum <chr>, genus <chr>, organismQuantityType <chr>,
#> #   species <chr>, associatedSequences <chr>, family <chr>, kingdomid <int>,
#> #   sampleSizeValue <chr>, eventDate <chr>, node_id <chr>, flags <chr>,
#> #   sss <dbl>, shoredistance <int>, sst <dbl>, bathymetry <int>, dna <list>,
#> #   hab <lgl>, brackish <lgl>

dna <- dna_records(occ, fields = c("scientificName"))

dna %>%
  select(scientificName, target_gene, DNA_sequence)
#> # A tibble: 1,136 x 3
#>    scientificName       target_gene DNA_sequence                                
#>    <chr>                <chr>       <chr>                                       
#>  1 Chrysochromulina st… 18S         GCTCCTACCGATTGAATGATCCGGTGAGCTTTTTGGACTGTGG…
#>  2 Phaeocystaceae       18S         GCTCCTACCGATTGAATGATCCGGTGAGGCCCCCGGACGGATT…
#>  3 Prymnesiaceae        18S         GCTCCTACCGATTGGACGATCCGGTGAAGCCTCCGGACTGTAG…
#>  4 Chrysochromulina sc… 18S         GCTCCTACCGATTGAATGATCCGGTGAGCTTTTTGGACTGTGG…
#>  5 Chrysochromulina st… 18S         GCTCCTACCGATTGAATGATCCGGTGAGCTTTTTGGACAGTGG…
#>  6 Chrysochromulina     18S         GCTCCTACCGATTGAATGATCCGGTGAGGCCCCCGGAATGGGA…
#>  7 Chrysochromulina     18S         GCTCCTACCGATTGAATGATCCGGTGAGGCCCCCGGAGTGGGA…
#>  8 Chrysochromulina sp… 18S         GCTCCTACCGATGGAAGGTTTAGGTGAGTTTTTCGGAGTTTTC…
#>  9 Chrysochromulina st… 18S         GCTCCTACCGATTGAATGATCCGGTGAGCTTTTTGGACAGTGG…
#> 10 Chrysochromulina st… 18S         GCTCCTACCGATTGAATGATCCGGTGAGCTTTTTGGACAGTGG…
#> # … with 1,126 more rows