# Advanced search and citation of occurrences

#### Hannah L. Owens

#### Cory Merow

#### Brian Maitner

#### Jamie M. Kass

#### Vijay Barve

#### Robert Guralnick

#### 2026-06-29

# Advanced features

This vignette demonstrates more advanced features and customization available in `occCite`. We recommend you read `vignette("Simple.Rmd", package = "occCite")` first, if you have not already done so.

## Loading data from previous GBIF searches

Querying GBIF can take quite a bit of time, especially for multiple species and/or well-known species. In this case, you may wish to access previously-downloaded data sets from your computer by specifying the general location of your downloaded `.zip` files. `occQuery` will crawl through your specified `GBIFDownloadDirectory` to collect all the `.zip` files contained in that folder and its subfolders. It will then import the most recent downloads that match your taxon list. These GBIF data will be appended to a BIEN search the same as if you do the simple real-time search (if you chose BIEN as well as GBIF), as was shown above. `checkPreviousGBIFDownload` is `TRUE` by default, but if `loadLocalGBIFDownload` is `TRUE`, `occQuery` will ignore `checkPreviousDownload`. It is also worth noting that `occCite` does not currently support mixed data download sources. That is, you cannot do GBIF queries for some taxa, download previously-prepared data sets for others, and load the rest from local data sets on your computer.

    # Simple search
    myOldOccCiteObject <- occQuery(x = "Protea cynaroides",
                                      datasources = c("gbif", "bien"),
                                      GBIFLogin = GBIFLogin,
                                      GBIFDownloadDirectory =
                                        system.file('extdata/', package='occCite'),
                                      checkPreviousGBIFDownload = T)

Here is the result. Look familiar?

    #GBIF search results
    head(myOldOccCiteObject@occResults$`Protea cynaroides`$GBIF$OccurrenceTable);

    ##                name longitude  latitude coordinateUncertaintyInMeters day month
    ## 1 Protea cynaroides  18.43928 -33.95440                             8  17     2
    ## 2 Protea cynaroides  22.12754 -33.91561                             4  11     2
    ## 3 Protea cynaroides  18.43927 -33.95429                             8  17     2
    ## 4 Protea cynaroides  18.43254 -34.29275                            31   6     2
    ## 5 Protea cynaroides  18.42429 -34.02934                          2167  10     2
    ## 6 Protea cynaroides  18.43529 -34.10545                             2   8     2
    ##   year                           datasetKey dataService
    ## 1 2022 50c9509d-22c7-4a22-a47d-8c48425ef4a7        GBIF
    ## 2 2022 50c9509d-22c7-4a22-a47d-8c48425ef4a7        GBIF
    ## 3 2022 50c9509d-22c7-4a22-a47d-8c48425ef4a7        GBIF
    ## 4 2022 50c9509d-22c7-4a22-a47d-8c48425ef4a7        GBIF
    ## 5 2022 50c9509d-22c7-4a22-a47d-8c48425ef4a7        GBIF
    ## 6 2022 50c9509d-22c7-4a22-a47d-8c48425ef4a7        GBIF
    ##                               datasetName
    ## 1 iNaturalist Research-grade Observations
    ## 2 iNaturalist Research-grade Observations
    ## 3 iNaturalist Research-grade Observations
    ## 4 iNaturalist Research-grade Observations
    ## 5 iNaturalist Research-grade Observations
    ## 6 iNaturalist Research-grade Observations

    #The full summary
    summary(myOldOccCiteObject)

    ##
    ##  OccCite query occurred on: 20 June, 2024
    ##
    ##  User query type: User-supplied list of taxa.
    ##
    ##  Sources for taxonomic rectification: GBIF Backbone Taxonomy
    ##
    ##
    ##  Taxonomic cleaning results:
    ##
    ##          Input Name                Best Match Taxonomic Databases w/ Matches
    ## 1 Protea cynaroides Protea cynaroides (L.) L.         GBIF Backbone Taxonomy
    ##
    ##  Sources for occurrence data: gbif, bien
    ##
    ##                     Species Occurrences Sources
    ## 1 Protea cynaroides (L.) L.        2334      17
    ##
    ##  GBIF dataset DOIs:
    ##
    ##                     Species GBIF Access Date           GBIF DOI
    ## 1 Protea cynaroides (L.) L.       2022-03-02 10.15468/dl.ztbx8c

Getting citation data works the exact same way with previously-downloaded data as it does from a fresh data set.

    #Get citations
    myOldOccCitations <- occCitation(myOldOccCiteObject)
    print(myOldOccCitations)

    ## Writing 5 Bibtex entries ... OK
    ## Results written to file 'temp.bib'

    ## AFFOUARD A, JOLY A, LOMBARDO J, CHAMP J, GOEAU H, CHOUET M, GRESSE H, BONNET P (2025). Pl@ntNet observations. Version 1.9. Pl@ntNet. https://doi.org/10.15468/gtebaa. Accessed via GBIF on 2022-03-02.
    ## AFFOUARD A, JOLY A, LOMBARDO J, CHAMP J, GOEAU H, CHOUET M, GRESSE H, BOTELLA C, BONNET P (2023). Pl@ntNet automatically identified occurrences. Version 1.8. Pl@ntNet. https://doi.org/10.15468/mma2ec. Accessed via GBIF on 2022-03-02.
    ## Chamberlain, S., Barve, V., Mcglinn, D., Oldoni, D., Desmet, P., Geffert, L., Ram, K. (2026). rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.8.5. https://CRAN.R-project.org/package = rgbif.
    ## Chamberlain, S., Boettiger, C. (2017). R Python, and Ruby clients for GBIF species occurrence data. PeerJ PrePrints.
    ## Fatima Parker-Allie, Ranwashe F (2018). PRECIS. South African National Biodiversity Institute. https://doi.org/10.15468/rckmn2. Accessed via GBIF on 2022-03-02.
    ## MNHN, Chagnoux S (2025). The vascular plants collection (P) at the Herbarium of the Muséum national d'Histoire Naturelle (MNHN - Paris). Version 69.422. MNHN - Museum national d'Histoire naturelle. https://doi.org/10.15468/nc6rxy. Accessed via GBIF on 2022-03-02.
    ## MNHN. Accessed via BIEN on NA.
    ## Maitner, B. (2026). . R package version 1.2.8. https://CRAN.R-project.org/package = BIEN.
    ## Missouri Botanical Garden,Herbarium. Accessed via BIEN on NA.
    ## Observation.org (2026). Observation.org, Nature data from around the World. https://doi.org/10.15468/5nilie. Accessed via GBIF on 2022-03-02.
    ## Owens, H., Merow, C., Maitner, B., Kass, J., Barve, V., Guralnick, R. (2026). occCite: Querying and Managing Large Biodiversity Occurrence Datasets. R package version 0.6.2. https://CRAN.R-project.org/package = occCite.
    ## Ranwashe F (2026). Botanical Database of Southern Africa (BODATSA): Botanical Collections. Version 1.31. South African National Biodiversity Institute. https://doi.org/10.15468/2aki0q. Accessed via GBIF on 2022-03-02.
    ## Rob Cubey (2022). Royal Botanic Garden Edinburgh Living Plant Collections (E). Royal Botanic Garden Edinburgh. https://doi.org/10.15468/bkzv1l. Accessed via GBIF on 2022-03-02.
    ## SANBI. Accessed via BIEN on NA.
    ## Senckenberg (2020). African Plants - a photo guide. https://doi.org/10.15468/r9azth. Accessed via GBIF on 2022-03-02.
    ## Taylor S (2019). G. S. Torrey Herbarium at the University of Connecticut (CONN). University of Connecticut. https://doi.org/10.15468/w35jmd. Accessed via GBIF on 2022-03-02.
    ## Team}, {.C. (2025). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
    ## Teisher J, Stimmel H (2026). Tropicos MO Specimen Data. Missouri Botanical Garden. https://doi.org/10.15468/hja69f. Accessed via GBIF on 2022-03-02.
    ## Tela Botanica. Carnet en Ligne. https://doi.org/10.15468/rydcn2. Accessed via GBIF on 2022-03-02.
    ## UConn. Accessed via BIEN on NA.
    ## iNaturalist contributors, iNaturalist (2026). iNaturalist Research-grade Observations. iNaturalist.org. https://doi.org/10.15468/ab3s5x. Accessed via GBIF on 2022-03-02.
    ## naturgucker.de. NABU|naturgucker. https://doi.org/10.15468/uc1apo. Accessed via GBIF on 2022-03-02.

Note that you can also load multiple species using either a vector of species names or a phylogeny (provided you have previously downloaded data for all of the species of interest), and you can load occurrences from non-GBIF data sources (e.g. BIEN) in the same query.

## Performing a Multi-Species Search

In addition to doing a simple, single species search, you can also use `occCite` to search for and manage occurrence datasets for multiple species. You can either submit a vector of species names, or you can submit a phylogeny! The occCitation function will return a named list of citation tables in the case of multiple species.

## occCite with a Phylogeny

Here is an example of how such a search is structured, using an unpublished phylogeny of billfishes.

    library(ape)
    #Get tree
    treeFile <- system.file("extdata/Fish_12Tax_time_calibrated.tre", package='occCite')
    phylogeny <- ape::read.nexus(treeFile)
    tree <- ape::extract.clade(phylogeny, 22)
    #Query databases for names
    myPhyOccCiteObject <- studyTaxonList(x = tree,
                                         datasources = "GBIF Backbone Taxonomy")

    ## handled warning: Following sources not found in
    ## Global Names Index source list: GBIF Backbone Taxonomy

    ## handled warning: No valid taxonomic data sources supplied.
    ## Populating default list from all available sources.

    ## handled warning: Following sources not found in
    ## Global Names Index source list: GBIF Backbone Taxonomy

    ## handled warning: No valid taxonomic data sources supplied.
    ## Populating default list from all available sources.

    ## handled warning: longer object length is not a multiple of shorter object length

    ## handled warning: Following sources not found in
    ## Global Names Index source list: GBIF Backbone Taxonomy

    ## handled warning: No valid taxonomic data sources supplied.
    ## Populating default list from all available sources.

    #Query GBIF for occurrence data
    myPhyOccCiteObject <- occQuery(x = myPhyOccCiteObject,
                                datasources = "gbif",
                                GBIFDownloadDirectory = system.file('extdata/', package='occCite'),
                                loadLocalGBIFDownload = T,
                                checkPreviousGBIFDownload = F)
    # What does a multispecies query look like?
    summary(myPhyOccCiteObject)

    ##
    ##  OccCite query occurred on: 29 June, 2026
    ##
    ##  User query type: User-supplied phylogeny.
    ##
    ##  Sources for taxonomic rectification: GBIF Backbone Taxonomy
    ##
    ##
    ##  Taxonomic cleaning results:
    ##
    ##                   Input Name                 Best Match
    ## 1 Tetrapturus_angustirostris Tetrapturus angustirostris
    ## 2         Tetrapturus_belone         Tetrapturus beloni
    ## 3         Tetrapturus_belone         Tetrapturus belone
    ## 4      Tetrapturus_pfluegeri      Tetrapturus pfluegeri
    ##                                                                                                                                                                                                                                                                                                                                                                                           Taxonomic Databases w/ Matches
    ## 1 Catalogue of Life; ITIS; WoRMS; IUCN; iNaturalist; TAXREF; Wikispecies; Catalog of Fishes; FishBase; FishBase Cache; Plazi; NZOR; GBIF Backbone Taxonomy; Catalogue of Life XR; Arctos; EOL; Open Tree of Life; MCZbase; Wikidata; IRMNG (old); NCBI; Papahanaumokuakea Marine National Monument; The National Checklist of Taiwan; CU*STAR; Bishop Museum; BioLib.cz; nlbif; ION; uBio NameBank; New Zealand Animalia
    ## 2                                                                                                                                                                                                                      ITIS; IUCN; TAXREF; FishBase; Sherborn Index Animalium; Plazi; Catalogue of Life XR; Arctos; EOL; Open Tree of Life; Wikidata; IRMNG (old); CU*STAR; BioLib.cz; ION; uBio NameBank; Bishop Museum
    ## 3                                                                                                                                                                                                                      ITIS; IUCN; TAXREF; FishBase; Sherborn Index Animalium; Plazi; Catalogue of Life XR; Arctos; EOL; Open Tree of Life; Wikidata; IRMNG (old); CU*STAR; BioLib.cz; ION; uBio NameBank; Bishop Museum
    ## 4                                                                                            Catalogue of Life; ITIS; WoRMS; IUCN; iNaturalist; TAXREF; Fauna of Brazil; Wikispecies; Catalog of Fishes; FishBase; FishBase Cache; Plazi; GBIF Backbone Taxonomy; Catalogue of Life XR; EUNIS; Arctos; EOL; Open Tree of Life; Wikidata; IRMNG (old); NCBI; CU*STAR; Bishop Museum; BioLib.cz; nlbif; ION; uBio NameBank
    ##
    ##  Sources for occurrence data: gbif
    ##
    ##                      Species Occurrences Sources
    ## 1 Tetrapturus angustirostris         649      23
    ## 2         Tetrapturus beloni           9       6
    ## 3         Tetrapturus belone           9       6
    ## 4      Tetrapturus pfluegeri         410       8
    ##
    ##  GBIF dataset DOIs:
    ##
    ##                      Species GBIF Access Date           GBIF DOI
    ## 1 Tetrapturus angustirostris       2019-07-04 10.15468/dl.mumi5e
    ## 2         Tetrapturus beloni       2019-07-04 10.15468/dl.q2nxb1
    ## 3         Tetrapturus belone       2019-07-04 10.15468/dl.q2nxb1
    ## 4      Tetrapturus pfluegeri       2019-07-04 10.15468/dl.qjidbs

When you have results for multiple species, as in this case, you can also plot the summary figures either for the whole search…

    plot(myPhyOccCiteObject)

or you can plot the results by species!

    plot(myPhyOccCiteObject, bySpecies = T, plotTypes = c("yearHistogram", "source"))

And then you can print out the citations, separated by species (or not, but in this example, they’re separate).

    #Get citations
    myPhyOccCitations <- occCitation(myPhyOccCiteObject)

    #Print citations as text with accession dates.
    print(myPhyOccCitations, bySpecies = T)

    ## Writing 6 Bibtex entries ... OK
    ## Results written to file 'temp.bib'

    ## Species: Tetrapturus angustirostris
    ##
    ## Australian Museum (2026). Australian Museum provider for OZCAM. https://doi.org/10.15468/e7susi. Accessed via GBIF on 2019-07-04.
    ## Barde J (2011). ecoscope_observation_database. IRD - Institute of Research for Development. https://doi.org/10.15468/dz1kk0. Accessed via GBIF on 2019-07-04.
    ## Bureau of Rural Sciences - National commercial fisheries half-degree data set 2000-2002 https://doi.org/10.15468/0esdv0. Accessed via GBIF on 2019-07-04.
    ## Cauquil P, Barde J (2011). observe_tuna_bycatch_ecoscope. IRD - Institute of Research for Development. https://doi.org/10.15468/23m361. Accessed via GBIF on 2019-07-04.
    ## Chamberlain, S., Barve, V., Mcglinn, D., Oldoni, D., Desmet, P., Geffert, L., Ram, K. (2026). rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.8.5. https://CRAN.R-project.org/package = rgbif.
    ## Chamberlain, S., Boettiger, C. (2017). R Python, and Ruby clients for GBIF species occurrence data. PeerJ PrePrints.
    ## Chiang W (2014). Taiwan Fisheries Research Institute – Digital archives of coastal and offshore specimens. TELDAP. https://doi.org/10.15468/xvxngy. Accessed via GBIF on 2019-07-04.
    ## European Nucleotide Archive (EMBL-EBI) (2019). Geographically tagged INSDC sequences. https://doi.org/10.15468/cndomv. Accessed via GBIF on 2019-07-04.
    ## Fong J (2026). CAS Ichthyology (ICH). Version 150.528. California Academy of Sciences. Occurrence dataset. http://ipt.calacademy.org:8080/resource?r=ich&v=150.528 https://doi.org/10.15468/efh2ib. Accessed via GBIF on 2019-07-04.
    ## Frable B (2025). SIO Marine Vertebrate Collection. Version 1.9. Scripps Institution of Oceanography. https://doi.org/10.15468/ad1ovc. Accessed via GBIF on 2019-07-04.
    ## Harvard University M, Morris P J (2026). Museum of Comparative Zoology, Harvard University. Version 162.514. Museum of Comparative Zoology, Harvard University. https://doi.org/10.15468/p5rupv. Accessed via GBIF on 2019-07-04.
    ## Inventaire National du Patrimoine Naturel (2018). Programme Ecoscope: données d'observations des écosystèmes marins exploités (Réunion). UAR PatriNat (OFB-MNHN-CNRS-IRD), Paris. https://doi.org/10.15468/elttrd. Accessed via GBIF on 2019-07-04.
    ## Inventaire National du Patrimoine Naturel (2018). Programme Ecoscope: données d'observations des écosystèmes marins exploités. UAR PatriNat (OFB-MNHN-CNRS-IRD), Paris. https://doi.org/10.15468/gdrknh. Accessed via GBIF on 2019-07-04.
    ## McLean, M.W. (2014). Straightforward Bibliography Management in R Using the RefManager Package. NA, NA. https://arxiv.org/abs/1403.2036.
    ## McLean, M.W. (2017). RefManageR: Import and Manage BibTeX and BibLaTeX References in R. The Journal of Open Source Software.
    ## Mertz W, Ludt W, Clardy T, Robson S, Camacho N (2026). LACM Vertebrate Collection. Version 18.20. Natural History Museum of Los Angeles County. https://doi.org/10.15468/77rmwd. Accessed via GBIF on 2019-07-04.
    ## Ministry for Primary Industries (2014). New Zealand research tagging database. Southwestern Pacific OBIS, National Institute of Water and Atmospheric Research (NIWA), Wellington, New Zealand, 411926 records, Online http://nzobisipt.niwa.co.nz/resource.do?r=mpi_tag released on November 5, 2014. https://doi.org/10.15468/i66xdm. Accessed via GBIF on 2019-07-04.
    ## Motomura H (2026). Fish collection of the Kagoshima University Museum. National Museum of Nature and Science, Japan. https://doi.org/10.15468/vcj3j8. Accessed via GBIF on 2019-07-04.
    ## Owens, H., Merow, C., Maitner, B., Kass, J., Barve, V., Guralnick, R. (2026). occCite: Querying and Managing Large Biodiversity Occurrence Datasets. R package version 0.6.2. https://CRAN.R-project.org/package = occCite.
    ## Queensland Museum (2026). Queensland Museum provider for OZCAM. https://doi.org/10.15468/lotsye. Accessed via GBIF on 2019-07-04.
    ## Raiva R, Santana P (2021). Diversidade e ocorrência de peixes em Inhambane (2009-2017). Version 1.7. National Institute of Fisheries Research (IIP) – Mozambique. https://doi.org/10.15468/4fj2tq. Accessed via GBIF on 2019-07-04.
    ## Raiva R, Viador R, Santana P (2021). Diversidade e ocorrência de peixes na Zambézia (2003-2016). Version 1.6. National Institute of Fisheries Research (IIP) – Mozambique. https://doi.org/10.15468/mrz36h. Accessed via GBIF on 2019-07-04.
    ## Robins R (2026). UF FLMNH Ichthyology. Version 117.556. Florida Museum of Natural History. https://doi.org/10.15468/8mjsel. Accessed via GBIF on 2019-07-04.
    ## South Australian Museum (2025). South Australian Museum Adelaide provider for OZCAM. https://doi.org/10.15468/wz4rrh. Accessed via GBIF on 2019-07-04.
    ## Team}, {.C. (2025). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
    ## The International Barcode of Life Consortium (2026). International Barcode of Life project (iBOL). https://doi.org/10.15468/inygc6. Accessed via GBIF on 2019-07-04.
    ## Uchifune Y, Yamamoto H (2026). Asia-Pacific Dataset. Version 1.39. National Museum of Nature and Science, Japan. https://doi.org/10.48518/00002. Accessed via GBIF on 2019-07-04.
    ## Western Australian Museum (2024). Western Australian Museum provider for OZCAM. https://doi.org/10.15468/5qt0dm. Accessed via GBIF on 2019-07-04.
    ## iNaturalist contributors, iNaturalist (2026). iNaturalist Research-grade Observations. iNaturalist.org. https://doi.org/10.15468/ab3s5x. Accessed via GBIF on 2019-07-04.

    ## Writing 6 Bibtex entries ... OK
    ## Results written to file 'temp.bib'

    ## Species: Tetrapturus beloni
    ##
    ## Chamberlain, S., Barve, V., Mcglinn, D., Oldoni, D., Desmet, P., Geffert, L., Ram, K. (2026). rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.8.5. https://CRAN.R-project.org/package = rgbif.
    ## Chamberlain, S., Boettiger, C. (2017). R Python, and Ruby clients for GBIF species occurrence data. PeerJ PrePrints.
    ## Conselleria de Medio Ambiente, Agua, Infraestructuras y Territorio. Generalitat Valenciana (2026). Banco de Datos de la Biodiversidad de la Comunitat Valenciana. Biodiversity data bank of Generalitat Valenciana. https://doi.org/10.15468/b4yqdy. Accessed via GBIF on 2019-07-04.
    ## European Nucleotide Archive (EMBL-EBI) (2019). Geographically tagged INSDC sequences. https://doi.org/10.15468/cndomv. Accessed via GBIF on 2019-07-04.
    ## Harvard University M, Morris P J (2026). Museum of Comparative Zoology, Harvard University. Version 162.514. Museum of Comparative Zoology, Harvard University. https://doi.org/10.15468/p5rupv. Accessed via GBIF on 2019-07-04.
    ## McLean, M.W. (2014). Straightforward Bibliography Management in R Using the RefManager Package. NA, NA. https://arxiv.org/abs/1403.2036.
    ## McLean, M.W. (2017). RefManageR: Import and Manage BibTeX and BibLaTeX References in R. The Journal of Open Source Software.
    ## Owens, H., Merow, C., Maitner, B., Kass, J., Barve, V., Guralnick, R. (2026). occCite: Querying and Managing Large Biodiversity Occurrence Datasets. R package version 0.6.2. https://CRAN.R-project.org/package = occCite.
    ## ROBERT S, LEPAREUR F, Inventaire National du Patrimoine Naturel (2022). Données d'occurrences Espèces issues de l'inventaire des ZNIEFF. Version 1.7. UAR PatriNat (OFB-MNHN-CNRS-IRD), Paris. https://doi.org/10.15468/ikshke. Accessed via GBIF on 2019-07-04.
    ## Robins R (2026). UF FLMNH Ichthyology. Version 117.556. Florida Museum of Natural History. https://doi.org/10.15468/8mjsel. Accessed via GBIF on 2019-07-04.
    ## Team}, {.C. (2025). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
    ## University of Kansas Biodiversity Institute: KUBI Ichthyology Collection https://doi.org/10.15468/mgjasg. Accessed via GBIF on 2019-07-04.

    ## Writing 6 Bibtex entries ... OK
    ## Results written to file 'temp.bib'

    ## Species: Tetrapturus belone
    ##
    ## Chamberlain, S., Barve, V., Mcglinn, D., Oldoni, D., Desmet, P., Geffert, L., Ram, K. (2026). rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.8.5. https://CRAN.R-project.org/package = rgbif.
    ## Chamberlain, S., Boettiger, C. (2017). R Python, and Ruby clients for GBIF species occurrence data. PeerJ PrePrints.
    ## Conselleria de Medio Ambiente, Agua, Infraestructuras y Territorio. Generalitat Valenciana (2026). Banco de Datos de la Biodiversidad de la Comunitat Valenciana. Biodiversity data bank of Generalitat Valenciana. https://doi.org/10.15468/b4yqdy. Accessed via GBIF on 2019-07-04.
    ## European Nucleotide Archive (EMBL-EBI) (2019). Geographically tagged INSDC sequences. https://doi.org/10.15468/cndomv. Accessed via GBIF on 2019-07-04.
    ## Harvard University M, Morris P J (2026). Museum of Comparative Zoology, Harvard University. Version 162.514. Museum of Comparative Zoology, Harvard University. https://doi.org/10.15468/p5rupv. Accessed via GBIF on 2019-07-04.
    ## McLean, M.W. (2014). Straightforward Bibliography Management in R Using the RefManager Package. NA, NA. https://arxiv.org/abs/1403.2036.
    ## McLean, M.W. (2017). RefManageR: Import and Manage BibTeX and BibLaTeX References in R. The Journal of Open Source Software.
    ## Owens, H., Merow, C., Maitner, B., Kass, J., Barve, V., Guralnick, R. (2026). occCite: Querying and Managing Large Biodiversity Occurrence Datasets. R package version 0.6.2. https://CRAN.R-project.org/package = occCite.
    ## ROBERT S, LEPAREUR F, Inventaire National du Patrimoine Naturel (2022). Données d'occurrences Espèces issues de l'inventaire des ZNIEFF. Version 1.7. UAR PatriNat (OFB-MNHN-CNRS-IRD), Paris. https://doi.org/10.15468/ikshke. Accessed via GBIF on 2019-07-04.
    ## Robins R (2026). UF FLMNH Ichthyology. Version 117.556. Florida Museum of Natural History. https://doi.org/10.15468/8mjsel. Accessed via GBIF on 2019-07-04.
    ## Team}, {.C. (2025). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
    ## University of Kansas Biodiversity Institute: KUBI Ichthyology Collection https://doi.org/10.15468/mgjasg. Accessed via GBIF on 2019-07-04.

    ## Writing 6 Bibtex entries ... OK
    ## Results written to file 'temp.bib'

    ## Species: Tetrapturus pfluegeri
    ##
    ## Barde J (2011). ecoscope_observation_database. IRD - Institute of Research for Development. https://doi.org/10.15468/dz1kk0. Accessed via GBIF on 2019-07-04.
    ## Boateng M (2021). Fishes of Ghana. Version 1.4. Department of Marine and Fisheries Sciences, University of Ghana. https://doi.org/10.15468/pgesnw. Accessed via GBIF on 2019-07-04.
    ## Cauquil P, Barde J (2011). observe_tuna_bycatch_ecoscope. IRD - Institute of Research for Development. https://doi.org/10.15468/23m361. Accessed via GBIF on 2019-07-04.
    ## Chamberlain, S., Barve, V., Mcglinn, D., Oldoni, D., Desmet, P., Geffert, L., Ram, K. (2026). rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.8.5. https://CRAN.R-project.org/package = rgbif.
    ## Chamberlain, S., Boettiger, C. (2017). R Python, and Ruby clients for GBIF species occurrence data. PeerJ PrePrints.
    ## European Nucleotide Archive (EMBL-EBI) (2019). Geographically tagged INSDC sequences. https://doi.org/10.15468/cndomv. Accessed via GBIF on 2019-07-04.
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