Introduction
We have entered the age of data-intensive scientific discovery. As
datasets increase in complexity and heterogeneity, we must preserve the
cycle of data citation from primary data sources to aggregating
databases to research products and back to primary data sources. The
citation cycle keeps science transparent, but it is also key to
supporting primary providers by documenting the use of their data. The
Global Biodiversity Information Facility and other data aggregators have
made great strides in harvesting citation data from research products
and linking them back to primary data providers. However, this only
works if those that publish research products cite primary data sources
in the first place. We developed occCite, a set of R-based tools for
downloading, managing, and citing biodiversity data, to advance toward
the goal of closing the data provenance cycle. These tools preserve
links between occurrence data and primary providers once researchers
download aggregated data, and facilitate the citation of primary data
providers in research papers.
The occCite work flow follows a three-step process. First, the user
inputs one or more taxonomic names (or a phylogeny). occCite then
rectifies those names by checking them against one or more taxonomic
databases, which can be specified by the user (see the Global Names
List). The results of the taxonomic
rectification are then kept in an occCiteData object in local memory.
Next, occCite takes the occCiteData object and user-defined search
parameters to query BIEN (through rbien) and/or GBIF(through rGBIF)
for records. The results are appended to the occCiteData object, along
with metadata on the search. Finally, the user can pass the
occCiteData object to occCitation, which compiles citations for the
primary providers, database aggregators, and R packages used to build
the dataset.
For an overview tutorial video of the package, see our YouTube video.
Future iterations of occCite will track citation data through the data
cleaning process and provide a series of visualizations on raw query
results and final datasets. It will also provide data citations in a
format congruent with best-practice recommendations for large
biodiversity datasets. Based on these data citation tools, we will also
propose a new set of standards for citing primary biodiversity data in
published research articles that provides due credit to contributors and
allows them to track the use of their work. Keep checking back!
Setup
If you plan to query GBIF, you will need to provide them with your user
login information. We have provided a dummy login below to show you the
format. You will need to provide actual account information. This is
because you will actually be downloading all of the records available
for the species using occ_download(), instead of getting results from
occ_search(), which has a hard limit of 200,000 occurrences.
library(occCite);
#Creating a GBIF login
GBIFLogin <- GBIFLoginManager(user = "occCiteTester",
email = "****@yahoo.com",
pwd = "12345")Performing a simple search
The basics
At its simplest, occCite allows you to search for occurrences for a
single species. The taxonomy of the user-specified species will be
verified using EOL and NCBI taxonomies by default.
# Simple search
mySimpleOccCiteObject <- occQuery(x = "Protea cynaroides",
datasources = c("gbif", "bien"),
GBIFLogin = GBIFLogin,
GBIFDownloadDirectory = paste0(path.package("occCite"), "/extdata/"),
checkPreviousGBIFDownload = T)Here is what the GBIF results look like:
# GBIF search results
head(mySimpleOccCiteObject@occResults$`Protea cynaroides`$GBIF$OccurrenceTable)## name longitude latitude day month year
## 1 Protea cynaroides 18.40540 -33.95891 4 1 2015
## 2 Protea cynaroides 18.42350 -33.96619 20 6 2019
## 3 Protea cynaroides 22.99340 -34.05478 16 6 2019
## 4 Protea cynaroides 18.40232 -34.08405 9 6 2019
## 5 Protea cynaroides 19.44807 -34.52123 13 6 2019
## 6 Protea cynaroides 18.39757 -34.07418 9 6 2019
## Dataset DatasetKey
## 1 iNaturalist research-grade observations 50c9509d-22c7-4a22-a47d-8c48425ef4a7
## 2 iNaturalist research-grade observations 50c9509d-22c7-4a22-a47d-8c48425ef4a7
## 3 iNaturalist research-grade observations 50c9509d-22c7-4a22-a47d-8c48425ef4a7
## 4 iNaturalist research-grade observations 50c9509d-22c7-4a22-a47d-8c48425ef4a7
## 5 iNaturalist research-grade observations 50c9509d-22c7-4a22-a47d-8c48425ef4a7
## 6 iNaturalist research-grade observations 50c9509d-22c7-4a22-a47d-8c48425ef4a7
## DataService
## 1 GBIF
## 2 GBIF
## 3 GBIF
## 4 GBIF
## 5 GBIF
## 6 GBIF
And here are the BIEN results:
#BIEN search results
head(mySimpleOccCiteObject@occResults$`Protea cynaroides`$BIEN$OccurrenceTable)## name longitude latitude day month year Dataset DatasetKey
## 1 Protea cynaroides 22.875 -33.875 20 8 1973 SANBI 2249
## 2 Protea cynaroides 25.125 -33.875 3 7 1934 SANBI 2249
## 3 Protea cynaroides 20.375 -33.875 16 8 1952 SANBI 2249
## 4 Protea cynaroides 21.375 -33.375 20 3 1947 SANBI 2249
## 5 Protea cynaroides 20.875 -34.125 21 6 1987 SANBI 2249
## 6 Protea cynaroides 24.625 -33.625 12 9 1973 SANBI 2249
## DataService
## 1 BIEN
## 2 BIEN
## 3 BIEN
## 4 BIEN
## 5 BIEN
## 6 BIEN
There is also a summary method for occCite objects with some basic
information about your search.
summary(mySimpleOccCiteObject)##
## OccCite query occurred on: 30 September, 2020
##
## User query type: User-supplied list of taxa.
##
## Sources for taxonomic rectification: NCBI
##
##
## Taxonomic cleaning results:
##
## Input Name Best Match Taxonomic Databases w/ Matches
## 1 Protea cynaroides Protea cynaroides NCBI
##
## Sources for occurrence data: gbif, bien
##
## Species Occurrences Sources
## 1 Protea cynaroides 828 15
##
## GBIF dataset DOIs:
##
## Species GBIF Access Date GBIF DOI
## 1 Protea cynaroides 2019-07-15 10.15468/dl.iqnra2
Simple citations
After doing a search for occurrence points, you can use occCitation()
to generate citations for primary biodiversity databases, as well as
database aggregators. Note: Currently, GBIF and BIEN are the only
aggregators for which citations are supported.
#Get citations
mySimpleOccCitations <- occCitation(mySimpleOccCiteObject)Here is a simple way of generating a formatted citation document from
the results of occCitation().
print(mySimpleOccCitations)## Warning in citation(x): no date field in DESCRIPTION file of package 'occCite'
## Warning in citation(x): could not determine year for 'occCite' from package
## DESCRIPTION file
## Warning: `as_data_frame()` is deprecated as of tibble 2.0.0.
## Please use `as_tibble()` instead.
## The signature and semantics have changed, see `?as_tibble`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
## Column `YEAR` contains character strings.
## No coercion to numeric applied.
## Cameron E, Auckland Museum A M (2020). Auckland Museum Botany Collection. Version 1.52. Auckland War Memorial Museum. https://doi.org/10.15468/mnjkvv. Accessed via GBIF on 2019-07-15.
## Capers R (2014). CONN. University of Connecticut. https://doi.org/10.15468/w35jmd. Accessed via GBIF on 2019-07-15.
## Chamberlain, S., Barve, V., Mcglinn, D., Oldoni, D., Desmet, P., Geffert, L., Ram, K. (2020). rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.3.0. 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 2019-07-15.
## Magill B, Solomon J, Stimmel H (2020). Tropicos Specimen Data. Missouri Botanical Garden. https://doi.org/10.15468/hja69f. Accessed via GBIF on 2019-07-15.
## Maitner, B. (2020). BIEN: Tools for Accessing the Botanical Information and Ecology. R package version 1.2.4. https://CRAN.R-project.org/package=BIEN.
## Missouri Botanical Garden,Herbarium. Accessed via BIEN on NA.
## MNHN, Chagnoux S (2020). The vascular plants collection (P) at the Herbarium of the Muséum national d'Histoire Naturelle (MNHN - Paris). Version 69.183. MNHN - Museum national d'Histoire naturelle. https://doi.org/10.15468/nc6rxy. Accessed via GBIF on 2019-07-15.
## MNHN. https://doi.org/10.15468/dl.fpwlzt. Accessed via BIEN on 2018-08-14.
## naturgucker.de. naturgucker. https://doi.org/10.15468/uc1apo. Accessed via GBIF on 2019-07-15.
## NSW. https://doi.org/10.15468/dl.fpwlzt. Accessed via BIEN on 2018-08-14.
## Owens, H.L., Merow, C., Maitner, B., Kass, J.M., Barve, V., Guralnick, R.P. (NA). occCite: Querying and Managing Large Biodiversity Occurrence Datasets. R package version 0.3.0. https://github.com/hannahlowens/occCite.
## R Core Team. (2019). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
## Ranwashe F (2019). BODATSA: Botanical Collections. Version 1.4. South African National Biodiversity Institute. https://doi.org/10.15468/2aki0q. Accessed via GBIF on 2019-07-15.
## SANBI. https://doi.org/10.15468/dl.fpwlzt. Accessed via BIEN on 2018-08-14.
## Senckenberg (2020). African Plants - a photo guide. https://doi.org/10.15468/r9azth. Accessed via GBIF on 2019-07-15.
## Tela Botanica. Carnet en Ligne. https://doi.org/10.15468/rydcn2. Accessed via GBIF on 2019-07-15.
## UConn. https://doi.org/10.15468/dl.fpwlzt. Accessed via BIEN on 2018-08-14.
## Ueda K (2020). iNaturalist Research-grade Observations. iNaturalist.org. https://doi.org/10.15468/ab3s5x. Accessed via GBIF on 2019-07-15.
Simple Taxonomic Rectification
In the simplest of searches, such as the one above, the taxonomy of your
input species name is automatically rectified through the occCite
function studyTaxonList() using gnr_resolve() from the taxize R
package. If you would like to change the source of the taxonomy being
used to rectify your species names, you can specify as many taxonomic
repositories as you like from the Global Names Index (GNI). The complete
list of GNI repositories can be found
here.
studyTaxonList() chooses the taxonomic names closest to those being
input and documents which taxonomic repositories agreed with those
names. studyTaxonList() instantiates an occCiteData object the same
way occQuery() does. This object can be passed into occQuery() to
perform your occurrence data search.
#Rectify taxonomy
myTROccCiteObject <- studyTaxonList(x = "Protea cynaroides",
datasources = c("NCBI", "EOL", "ITIS"))
myTROccCiteObject@cleanedTaxonomy## Input Name Best Match Taxonomic Databases w/ Matches
## 1 Protea cynaroides Protea cynaroides NCBI
Advanced features
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 load
myOldOccCiteObject <- occQuery(x = "Protea cynaroides",
datasources = c("gbif", "bien"),
GBIFLogin = NULL,
GBIFDownloadDirectory = paste0(path.package("occCite"), "/extdata/"),
loadLocalGBIFDownload = T,
checkPreviousGBIFDownload = F)Here is the result. Look familiar?
#GBIF search results
head(myOldOccCiteObject@occResults$`Protea cynaroides`$GBIF$OccurrenceTable)## name longitude latitude day month year
## 1 Protea cynaroides 18.40540 -33.95891 4 1 2015
## 2 Protea cynaroides 18.42350 -33.96619 20 6 2019
## 3 Protea cynaroides 22.99340 -34.05478 16 6 2019
## 4 Protea cynaroides 18.40232 -34.08405 9 6 2019
## 5 Protea cynaroides 19.44807 -34.52123 13 6 2019
## 6 Protea cynaroides 18.39757 -34.07418 9 6 2019
## Dataset DatasetKey
## 1 iNaturalist research-grade observations 50c9509d-22c7-4a22-a47d-8c48425ef4a7
## 2 iNaturalist research-grade observations 50c9509d-22c7-4a22-a47d-8c48425ef4a7
## 3 iNaturalist research-grade observations 50c9509d-22c7-4a22-a47d-8c48425ef4a7
## 4 iNaturalist research-grade observations 50c9509d-22c7-4a22-a47d-8c48425ef4a7
## 5 iNaturalist research-grade observations 50c9509d-22c7-4a22-a47d-8c48425ef4a7
## 6 iNaturalist research-grade observations 50c9509d-22c7-4a22-a47d-8c48425ef4a7
## DataService
## 1 GBIF
## 2 GBIF
## 3 GBIF
## 4 GBIF
## 5 GBIF
## 6 GBIF
#The full summary
summary(myOldOccCiteObject)##
## OccCite query occurred on: 30 September, 2020
##
## User query type: User-supplied list of taxa.
##
## Sources for taxonomic rectification: NCBI
##
##
## Taxonomic cleaning results:
##
## Input Name Best Match Taxonomic Databases w/ Matches
## 1 Protea cynaroides Protea cynaroides NCBI
##
## Sources for occurrence data: gbif, bien
##
## Species Occurrences Sources
## 1 Protea cynaroides 828 15
##
## GBIF dataset DOIs:
##
## Species GBIF Access Date GBIF DOI
## 1 Protea cynaroides 2019-07-15 10.15468/dl.iqnra2
Getting citation data works the exact same way with previously-downloaded data as it does from a fresh dataset.
#Get citations
myOldOccCitations <- occCitation(myOldOccCiteObject)
print(myOldOccCitations)## Warning in citation(x): no date field in DESCRIPTION file of package 'occCite'
## Warning in citation(x): could not determine year for 'occCite' from package
## DESCRIPTION file
## Column `YEAR` contains character strings.
## No coercion to numeric applied.
## Cameron E, Auckland Museum A M (2020). Auckland Museum Botany Collection. Version 1.52. Auckland War Memorial Museum. https://doi.org/10.15468/mnjkvv. Accessed via GBIF on 2019-07-15.
## Capers R (2014). CONN. University of Connecticut. https://doi.org/10.15468/w35jmd. Accessed via GBIF on 2019-07-15.
## Chamberlain, S., Barve, V., Mcglinn, D., Oldoni, D., Desmet, P., Geffert, L., Ram, K. (2020). rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.3.0. 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 2019-07-15.
## Magill B, Solomon J, Stimmel H (2020). Tropicos Specimen Data. Missouri Botanical Garden. https://doi.org/10.15468/hja69f. Accessed via GBIF on 2019-07-15.
## Maitner, B. (2020). BIEN: Tools for Accessing the Botanical Information and Ecology. R package version 1.2.4. https://CRAN.R-project.org/package=BIEN.
## Missouri Botanical Garden,Herbarium. Accessed via BIEN on NA.
## MNHN, Chagnoux S (2020). The vascular plants collection (P) at the Herbarium of the Muséum national d'Histoire Naturelle (MNHN - Paris). Version 69.183. MNHN - Museum national d'Histoire naturelle. https://doi.org/10.15468/nc6rxy. Accessed via GBIF on 2019-07-15.
## MNHN. https://doi.org/10.15468/dl.fpwlzt. Accessed via BIEN on 2018-08-14.
## naturgucker.de. naturgucker. https://doi.org/10.15468/uc1apo. Accessed via GBIF on 2019-07-15.
## NSW. https://doi.org/10.15468/dl.fpwlzt. Accessed via BIEN on 2018-08-14.
## Owens, H.L., Merow, C., Maitner, B., Kass, J.M., Barve, V., Guralnick, R.P. (NA). occCite: Querying and Managing Large Biodiversity Occurrence Datasets. R package version 0.3.0. https://github.com/hannahlowens/occCite.
## R Core Team. (2019). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
## Ranwashe F (2019). BODATSA: Botanical Collections. Version 1.4. South African National Biodiversity Institute. https://doi.org/10.15468/2aki0q. Accessed via GBIF on 2019-07-15.
## SANBI. https://doi.org/10.15468/dl.fpwlzt. Accessed via BIEN on 2018-08-14.
## Senckenberg (2020). African Plants - a photo guide. https://doi.org/10.15468/r9azth. Accessed via GBIF on 2019-07-15.
## Tela Botanica. Carnet en Ligne. https://doi.org/10.15468/rydcn2. Accessed via GBIF on 2019-07-15.
## UConn. https://doi.org/10.15468/dl.fpwlzt. Accessed via BIEN on 2018-08-14.
## Ueda K (2020). iNaturalist Research-grade Observations. iNaturalist.org. https://doi.org/10.15468/ab3s5x. Accessed via GBIF on 2019-07-15.
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!
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, 18)
#Query databases for names
myPhyOccCiteObject <- studyTaxonList(x = tree, datasources = "NCBI")
#Query GBIF for occurrence data
myPhyOccCiteObject <- occQuery(x = myPhyOccCiteObject,
datasources = "gbif",
GBIFDownloadDirectory = paste0(path.package("occCite"), "/extdata/"),
loadLocalGBIFDownload = T,
checkPreviousGBIFDownload = F)# What does a multispecies query look like?
summary(myPhyOccCiteObject)##
## OccCite query occurred on: 30 September, 2020
##
## User query type: User-supplied phylogeny.
##
## Sources for taxonomic rectification: NCBI
##
##
## Taxonomic cleaning results:
##
## Input Name Best Match
## 1 Istiompax_indica Istiompax indica
## 2 Kajikia_albida Kajikia albida
## 3 Kajikia_audax Kajikia audax
## 4 Tetrapturus_angustirostris Tetrapturus angustirostris
## 5 Tetrapturus_belone Tetrapturus belone
## 6 Tetrapturus_georgii Tetrapturus georgii
## 7 Tetrapturus_pfluegeri Tetrapturus pfluegeri
## Taxonomic Databases w/ Matches
## 1 NCBI
## 2 NCBI
## 3 NCBI
## 4 NCBI
## 5 NCBI
## 6 NCBI
## 7 NCBI
##
## Sources for occurrence data: gbif
##
## Species Occurrences Sources
## 1 Istiompax indica 468 23
## 2 Kajikia albida 167 16
## 3 Kajikia audax 6721 22
## 4 Tetrapturus angustirostris 174 22
## 5 Tetrapturus belone 9 6
## 6 Tetrapturus georgii 62 4
## 7 Tetrapturus pfluegeri 409 7
##
## GBIF dataset DOIs:
##
## Species GBIF Access Date GBIF DOI
## 1 Istiompax indica 2019-07-04 10.15468/dl.crapuf
## 2 Kajikia albida 2019-07-04 10.15468/dl.lnwf6a
## 3 Kajikia audax 2019-07-04 10.15468/dl.txromp
## 4 Tetrapturus angustirostris 2019-07-04 10.15468/dl.mumi5e
## 5 Tetrapturus belone 2019-07-04 10.15468/dl.q2nxb1
## 6 Tetrapturus georgii 2019-07-04 10.15468/dl.h860up
## 7 Tetrapturus pfluegeri 2019-07-04 10.15468/dl.qjidbs
#Get citations
myPhyOccCitations <- occCitation(myPhyOccCiteObject)
#Print citations as text with accession dates.
print(myPhyOccCitations)## Warning in citation(x): no date field in DESCRIPTION file of package 'occCite'
## Warning in citation(x): could not determine year for 'occCite' from package
## DESCRIPTION file
## Column `YEAR` contains character strings.
## No coercion to numeric applied.
## Australian Museum (2020). 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.
## BARDE Julien N, Inventaire National du Patrimoine Naturel (2019). Programme Ecoscope: données d'observations des écosystèmes marins exploités (Réunion). Version 1.1. UMS PatriNat (OFB-CNRS-MNHN), Paris. https://doi.org/10.15468/elttrd. Accessed via GBIF on 2019-07-04.
## BARDE Julien N, Inventaire National du Patrimoine Naturel (2019). Programme Ecoscope: données d'observations des écosystèmes marins exploités. Version 1.1. UMS PatriNat (OFB-CNRS-MNHN), Paris. https://doi.org/10.15468/gdrknh. Accessed via GBIF on 2019-07-04.
## Bentley A (2020). KUBI Ichthyology Collection. Version 17.67. University of Kansas Biodiversity Institute. https://doi.org/10.15468/mgjasg. Accessed via GBIF on 2019-07-04.
## Bentley A (2020). KUBI Ichthyology Tissue Collection. Version 18.55. University of Kansas Biodiversity Institute. https://doi.org/10.15468/jmsnwg. Accessed via GBIF on 2019-07-04.
## Casassovici A, Brosens D (2020). Diveboard - Scuba diving citizen science observations. Version 54.29. Diveboard. https://doi.org/10.15468/tnjrgy. Accessed via GBIF on 2019-07-04.
## Catania D, Fong J (2020). CAS Ichthyology (ICH). Version 150.242. California Academy of Sciences. https://doi.org/10.15468/efh2ib. 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. (2020). rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.3.0. 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.
## Commonwealth Scientific and Industrial Research Organisation (2020). CSIRO Ichthyology provider for OZCAM. https://doi.org/10.15468/azp1pf. Accessed via GBIF on 2019-07-04.
## CSIRO Oceans and Atmosphere (2020). CSIRO, Rachel Cruises, Shark Data, Arafura Sea, North Australia, 1984. Version 6.2. https://doi.org/10.15468/yickr6. Accessed via GBIF on 2019-07-04.
## Elías Gutiérrez M, Comisión nacional para el conocimiento y uso de la biodiversidad C (2020). Códigos de barras de la vida en peces y zooplancton de México. Version 1.7. Comisión nacional para el conocimiento y uso de la biodiversidad. https://doi.org/10.15468/xmbkgo. 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.
## Feeney R (2019). LACM Vertebrate Collection. Version 18.7. Natural History Museum of Los Angeles County. https://doi.org/10.15468/77rmwd. Accessed via GBIF on 2019-07-04.
## Frable B (2019). SIO Marine Vertebrate Collection. Version 1.7. Scripps Institution of Oceanography. https://doi.org/10.15468/ad1ovc. Accessed via GBIF on 2019-07-04.
## González Acosta A F, Comisión nacional para el conocimiento y uso de la biodiversidad C (2020). Ampliación de la base de datos de la ictiofauna insular del Golfo de California. Version 1.7. Comisión nacional para el conocimiento y uso de la biodiversidad. https://doi.org/10.15468/p5ovq7. Accessed via GBIF on 2019-07-04.
## Grant S, McMahan C (2020). Field Museum of Natural History (Zoology) Fish Collection. Version 13.12. Field Museum. https://doi.org/10.15468/alz7wu. Accessed via GBIF on 2019-07-04.
## Harvard University M, Morris P J (2020). Museum of Comparative Zoology, Harvard University. Version 162.228. Museum of Comparative Zoology, Harvard University. https://doi.org/10.15468/p5rupv. Accessed via GBIF on 2019-07-04.
## Mackay K (2018). New Zealand research tagging database. Version 1.5. Southwestern Pacific Ocean Biogeographic Information System (OBIS) Node. https://doi.org/10.15468/i66xdm. Accessed via GBIF on 2019-07-04.
## Mackay K (2019). Marine biological observation data from coastal and offshore surveys around New Zealand. Version 1.8. The National Institute of Water and Atmospheric Research (NIWA). https://doi.org/10.15468/pzpgop. Accessed via GBIF on 2019-07-04.
## Maslenikov K (2019). UWFC Ichthyology Collection. University of Washington Burke Museum. https://doi.org/10.15468/vvp7gr. Accessed via GBIF on 2019-07-04.
## Millen B (2019). Ichthyology Collection - Royal Ontario Museum. Version 18.7. Royal Ontario Museum. https://doi.org/10.15468/syisbx. Accessed via GBIF on 2019-07-04.
## Museum and Art Gallery of the Northern Territory (2019). Northern Territory Museum and Art Gallery provider for OZCAM. https://doi.org/10.15468/giro3a. Accessed via GBIF on 2019-07-04.
## Museums Victoria (2020). Museums Victoria provider for OZCAM. https://doi.org/10.15468/lp1ctu. Accessed via GBIF on 2019-07-04.
## National Museum of Nature and Science, Japan (2020). Fish specimens of Kagoshima University Museum. https://doi.org/10.15468/vcj3j8. Accessed via GBIF on 2019-07-04.
## Orrell T (2020). NMNH Extant Specimen Records. Version 1.35. National Museum of Natural History, Smithsonian Institution. https://doi.org/10.15468/hnhrg3. Accessed via GBIF on 2019-07-04.
## Owens, H.L., Merow, C., Maitner, B., Kass, J.M., Barve, V., Guralnick, R.P. (NA). occCite: Querying and Managing Large Biodiversity Occurrence Datasets. R package version 0.3.0. https://github.com/hannahlowens/occCite.
## Pozo de la Tijera M D C, Comisión nacional para el conocimiento y uso de la biodiversidad C (2020). Fortalecimiento de las colecciones de ECOSUR. Primera fase (Ictioplancton Chetumal). Version 1.3. Comisión nacional para el conocimiento y uso de la biodiversidad. https://doi.org/10.15468/orx3mk. Accessed via GBIF on 2019-07-04.
## Queensland Museum (2020). Queensland Museum provider for OZCAM. https://doi.org/10.15468/lotsye. Accessed via GBIF on 2019-07-04.
## R Core Team. (2019). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
## Raiva R, Santana P (2019). Diversidade e ocorrência de peixes em Inhambane (2009-2017). Version 1.4. 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 (2019). Diversidade e ocorrência de peixes na Zambézia (2003-2016). National Institute of Fisheries Research (IIP) – Mozambique. https://doi.org/10.15468/mrz36h. Accessed via GBIF on 2019-07-04.
## Ranz J (2017). 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.
## Robins R (2020). UF FLMNH Ichthyology. Version 117.280. Florida Museum of Natural History. https://doi.org/10.15468/8mjsel. Accessed via GBIF on 2019-07-04.
## Sánchez González S, Comisión nacional para el conocimiento y uso de la biodiversidad C (2020). Taxonomía y sistemática de la Ictiofauna de la Bahía de Banderas del Estado de Nayarit, México. Comisión nacional para el conocimiento y uso de la biodiversidad. https://doi.org/10.15468/uhrwsl. Accessed via GBIF on 2019-07-04.
## Shane G (2018). Pelagic fish food web linkages, Queensland, Australia (2003-2005). CSIRO Oceans and Atmosphere. https://doi.org/10.15468/yy5wdp. Accessed via GBIF on 2019-07-04.
## South Australian Museum (2020). South Australian Museum Australia provider for OZCAM. https://doi.org/10.15468/wz4rrh. Accessed via GBIF on 2019-07-04.
## The International Barcode of Life Consortium (2016). International Barcode of Life project (iBOL). https://doi.org/10.15468/inygc6. Accessed via GBIF on 2019-07-04.
## Uchifune Y, Yamamoto H (2020). Asia-Pacific Dataset. Version 1.33. National Museum of Nature and Science, Japan. https://doi.org/10.15468/vjeh1p. Accessed via GBIF on 2019-07-04.
## Ueda K (2020). iNaturalist Research-grade Observations. iNaturalist.org. https://doi.org/10.15468/ab3s5x. Accessed via GBIF on 2019-07-04.
## UMS PatriNat (OFB-CNRS-MNHN), Paris (2020). Données d'occurrences Espèces issues de l'inventaire des ZNIEFF. Version 1.1. https://doi.org/10.15468/ikshke. Accessed via GBIF on 2019-07-04.
## Western Australian Museum (2019). Western Australian Museum provider for OZCAM. https://doi.org/10.15468/5qt0dm. Accessed via GBIF on 2019-07-04.
It is also possible to print citations separated by species.
print(myPhyOccCitations, bySpecies = T)## Warning in citation(x): no date field in DESCRIPTION file of package 'occCite'
## Warning in citation(x): could not determine year for 'occCite' from package
## DESCRIPTION file
## Column `YEAR` contains character strings.
## No coercion to numeric applied.
## Species: Istiompax indica
##
## Australian Museum (2020). 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.
## BARDE Julien N, Inventaire National du Patrimoine Naturel (2019). Programme Ecoscope: données d'observations des écosystèmes marins exploités (Réunion). Version 1.1. UMS PatriNat (OFB-CNRS-MNHN), Paris. https://doi.org/10.15468/elttrd. Accessed via GBIF on 2019-07-04.
## Catania D, Fong J (2020). CAS Ichthyology (ICH). Version 150.242. California Academy of Sciences. https://doi.org/10.15468/efh2ib. 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. (2020). rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.3.0. https://CRAN.R-project.org/package=rgbif.
## Chamberlain, S., Boettiger, C. (2017). R Python, and Ruby clients for GBIF species occurrence data. PeerJ PrePrints.
## CSIRO Oceans and Atmosphere (2020). CSIRO, Rachel Cruises, Shark Data, Arafura Sea, North Australia, 1984. Version 6.2. https://doi.org/10.15468/yickr6. Accessed via GBIF on 2019-07-04.
## Elías Gutiérrez M, Comisión nacional para el conocimiento y uso de la biodiversidad C (2020). Códigos de barras de la vida en peces y zooplancton de México. Version 1.7. Comisión nacional para el conocimiento y uso de la biodiversidad. https://doi.org/10.15468/xmbkgo. 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.
## Feeney R (2019). LACM Vertebrate Collection. Version 18.7. Natural History Museum of Los Angeles County. https://doi.org/10.15468/77rmwd. Accessed via GBIF on 2019-07-04.
## Mackay K (2018). New Zealand research tagging database. Version 1.5. Southwestern Pacific Ocean Biogeographic Information System (OBIS) Node. https://doi.org/10.15468/i66xdm. Accessed via GBIF on 2019-07-04.
## Museum and Art Gallery of the Northern Territory (2019). Northern Territory Museum and Art Gallery provider for OZCAM. https://doi.org/10.15468/giro3a. Accessed via GBIF on 2019-07-04.
## Museums Victoria (2020). Museums Victoria provider for OZCAM. https://doi.org/10.15468/lp1ctu. Accessed via GBIF on 2019-07-04.
## Owens, H.L., Merow, C., Maitner, B., Kass, J.M., Barve, V., Guralnick, R.P. (NA). occCite: Querying and Managing Large Biodiversity Occurrence Datasets. R package version 0.3.0. https://github.com/hannahlowens/occCite.
## Pozo de la Tijera M D C, Comisión nacional para el conocimiento y uso de la biodiversidad C (2020). Fortalecimiento de las colecciones de ECOSUR. Primera fase (Ictioplancton Chetumal). Version 1.3. Comisión nacional para el conocimiento y uso de la biodiversidad. https://doi.org/10.15468/orx3mk. Accessed via GBIF on 2019-07-04.
## Queensland Museum (2020). Queensland Museum provider for OZCAM. https://doi.org/10.15468/lotsye. Accessed via GBIF on 2019-07-04.
## R Core Team. (2019). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
## Raiva R, Santana P (2019). Diversidade e ocorrência de peixes em Inhambane (2009-2017). Version 1.4. 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 (2019). Diversidade e ocorrência de peixes na Zambézia (2003-2016). National Institute of Fisheries Research (IIP) – Mozambique. https://doi.org/10.15468/mrz36h. Accessed via GBIF on 2019-07-04.
## Robins R (2020). UF FLMNH Ichthyology. Version 117.280. Florida Museum of Natural History. https://doi.org/10.15468/8mjsel. Accessed via GBIF on 2019-07-04.
## Sánchez González S, Comisión nacional para el conocimiento y uso de la biodiversidad C (2020). Taxonomía y sistemática de la Ictiofauna de la Bahía de Banderas del Estado de Nayarit, México. Comisión nacional para el conocimiento y uso de la biodiversidad. https://doi.org/10.15468/uhrwsl. Accessed via GBIF on 2019-07-04.
## Shane G (2018). Pelagic fish food web linkages, Queensland, Australia (2003-2005). CSIRO Oceans and Atmosphere. https://doi.org/10.15468/yy5wdp. Accessed via GBIF on 2019-07-04.
## The International Barcode of Life Consortium (2016). International Barcode of Life project (iBOL). https://doi.org/10.15468/inygc6. Accessed via GBIF on 2019-07-04.
## Uchifune Y, Yamamoto H (2020). Asia-Pacific Dataset. Version 1.33. National Museum of Nature and Science, Japan. https://doi.org/10.15468/vjeh1p. Accessed via GBIF on 2019-07-04.
## Ueda K (2020). iNaturalist Research-grade Observations. iNaturalist.org. https://doi.org/10.15468/ab3s5x. Accessed via GBIF on 2019-07-04.
## Western Australian Museum (2019). Western Australian Museum provider for OZCAM. https://doi.org/10.15468/5qt0dm. Accessed via GBIF on 2019-07-04.
## Warning in citation(x): no date field in DESCRIPTION file of package 'occCite'
## Warning in citation(x): could not determine year for 'occCite' from package
## DESCRIPTION file
## Column `YEAR` contains character strings.
## No coercion to numeric applied.
## Species: Kajikia albida
##
## 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.
## BARDE Julien N, Inventaire National du Patrimoine Naturel (2019). Programme Ecoscope: données d'observations des écosystèmes marins exploités. Version 1.1. UMS PatriNat (OFB-CNRS-MNHN), Paris. https://doi.org/10.15468/gdrknh. Accessed via GBIF on 2019-07-04.
## Bentley A (2020). KUBI Ichthyology Collection. Version 17.67. University of Kansas Biodiversity Institute. https://doi.org/10.15468/mgjasg. Accessed via GBIF on 2019-07-04.
## Bentley A (2020). KUBI Ichthyology Tissue Collection. Version 18.55. University of Kansas Biodiversity Institute. https://doi.org/10.15468/jmsnwg. Accessed via GBIF on 2019-07-04.
## Casassovici A, Brosens D (2020). Diveboard - Scuba diving citizen science observations. Version 54.29. Diveboard. https://doi.org/10.15468/tnjrgy. 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. (2020). rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.3.0. https://CRAN.R-project.org/package=rgbif.
## Chamberlain, S., Boettiger, C. (2017). R Python, and Ruby clients for GBIF species occurrence data. PeerJ PrePrints.
## Elías Gutiérrez M, Comisión nacional para el conocimiento y uso de la biodiversidad C (2020). Códigos de barras de la vida en peces y zooplancton de México. Version 1.7. Comisión nacional para el conocimiento y uso de la biodiversidad. https://doi.org/10.15468/xmbkgo. 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.
## Feeney R (2019). LACM Vertebrate Collection. Version 18.7. Natural History Museum of Los Angeles County. https://doi.org/10.15468/77rmwd. Accessed via GBIF on 2019-07-04.
## Frable B (2019). SIO Marine Vertebrate Collection. Version 1.7. Scripps Institution of Oceanography. https://doi.org/10.15468/ad1ovc. Accessed via GBIF on 2019-07-04.
## Harvard University M, Morris P J (2020). Museum of Comparative Zoology, Harvard University. Version 162.228. Museum of Comparative Zoology, Harvard University. https://doi.org/10.15468/p5rupv. Accessed via GBIF on 2019-07-04.
## Millen B (2019). Ichthyology Collection - Royal Ontario Museum. Version 18.7. Royal Ontario Museum. https://doi.org/10.15468/syisbx. Accessed via GBIF on 2019-07-04.
## Orrell T (2020). NMNH Extant Specimen Records. Version 1.35. National Museum of Natural History, Smithsonian Institution. https://doi.org/10.15468/hnhrg3. Accessed via GBIF on 2019-07-04.
## Owens, H.L., Merow, C., Maitner, B., Kass, J.M., Barve, V., Guralnick, R.P. (NA). occCite: Querying and Managing Large Biodiversity Occurrence Datasets. R package version 0.3.0. https://github.com/hannahlowens/occCite.
## Pozo de la Tijera M D C, Comisión nacional para el conocimiento y uso de la biodiversidad C (2020). Fortalecimiento de las colecciones de ECOSUR. Primera fase (Ictioplancton Chetumal). Version 1.3. Comisión nacional para el conocimiento y uso de la biodiversidad. https://doi.org/10.15468/orx3mk. Accessed via GBIF on 2019-07-04.
## R Core Team. (2019). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
## Robins R (2020). UF FLMNH Ichthyology. Version 117.280. Florida Museum of Natural History. https://doi.org/10.15468/8mjsel. Accessed via GBIF on 2019-07-04.
## The International Barcode of Life Consortium (2016). International Barcode of Life project (iBOL). https://doi.org/10.15468/inygc6. Accessed via GBIF on 2019-07-04.
## Warning in citation(x): no date field in DESCRIPTION file of package 'occCite'
## Warning in citation(x): could not determine year for 'occCite' from package
## DESCRIPTION file
## Column `YEAR` contains character strings.
## No coercion to numeric applied.
## Species: Kajikia audax
##
## Australian Museum (2020). 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.
## BARDE Julien N, Inventaire National du Patrimoine Naturel (2019). Programme Ecoscope: données d'observations des écosystèmes marins exploités (Réunion). Version 1.1. UMS PatriNat (OFB-CNRS-MNHN), Paris. https://doi.org/10.15468/elttrd. Accessed via GBIF on 2019-07-04.
## BARDE Julien N, Inventaire National du Patrimoine Naturel (2019). Programme Ecoscope: données d'observations des écosystèmes marins exploités. Version 1.1. UMS PatriNat (OFB-CNRS-MNHN), Paris. https://doi.org/10.15468/gdrknh. 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. (2020). rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.3.0. 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.
## Commonwealth Scientific and Industrial Research Organisation (2020). CSIRO Ichthyology provider for OZCAM. https://doi.org/10.15468/azp1pf. 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.
## Feeney R (2019). LACM Vertebrate Collection. Version 18.7. Natural History Museum of Los Angeles County. https://doi.org/10.15468/77rmwd. Accessed via GBIF on 2019-07-04.
## Frable B (2019). SIO Marine Vertebrate Collection. Version 1.7. Scripps Institution of Oceanography. https://doi.org/10.15468/ad1ovc. Accessed via GBIF on 2019-07-04.
## González Acosta A F, Comisión nacional para el conocimiento y uso de la biodiversidad C (2020). Ampliación de la base de datos de la ictiofauna insular del Golfo de California. Version 1.7. Comisión nacional para el conocimiento y uso de la biodiversidad. https://doi.org/10.15468/p5ovq7. Accessed via GBIF on 2019-07-04.
## Grant S, McMahan C (2020). Field Museum of Natural History (Zoology) Fish Collection. Version 13.12. Field Museum. https://doi.org/10.15468/alz7wu. Accessed via GBIF on 2019-07-04.
## Mackay K (2018). New Zealand research tagging database. Version 1.5. Southwestern Pacific Ocean Biogeographic Information System (OBIS) Node. https://doi.org/10.15468/i66xdm. Accessed via GBIF on 2019-07-04.
## Mackay K (2019). Marine biological observation data from coastal and offshore surveys around New Zealand. Version 1.8. The National Institute of Water and Atmospheric Research (NIWA). https://doi.org/10.15468/pzpgop. Accessed via GBIF on 2019-07-04.
## Maslenikov K (2019). UWFC Ichthyology Collection. University of Washington Burke Museum. https://doi.org/10.15468/vvp7gr. Accessed via GBIF on 2019-07-04.
## Museum and Art Gallery of the Northern Territory (2019). Northern Territory Museum and Art Gallery provider for OZCAM. https://doi.org/10.15468/giro3a. Accessed via GBIF on 2019-07-04.
## Orrell T (2020). NMNH Extant Specimen Records. Version 1.35. National Museum of Natural History, Smithsonian Institution. https://doi.org/10.15468/hnhrg3. Accessed via GBIF on 2019-07-04.
## Owens, H.L., Merow, C., Maitner, B., Kass, J.M., Barve, V., Guralnick, R.P. (NA). occCite: Querying and Managing Large Biodiversity Occurrence Datasets. R package version 0.3.0. https://github.com/hannahlowens/occCite.
## Queensland Museum (2020). Queensland Museum provider for OZCAM. https://doi.org/10.15468/lotsye. Accessed via GBIF on 2019-07-04.
## R Core Team. (2019). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
## Robins R (2020). UF FLMNH Ichthyology. Version 117.280. Florida Museum of Natural History. https://doi.org/10.15468/8mjsel. Accessed via GBIF on 2019-07-04.
## The International Barcode of Life Consortium (2016). International Barcode of Life project (iBOL). https://doi.org/10.15468/inygc6. Accessed via GBIF on 2019-07-04.
## Uchifune Y, Yamamoto H (2020). Asia-Pacific Dataset. Version 1.33. National Museum of Nature and Science, Japan. https://doi.org/10.15468/vjeh1p. Accessed via GBIF on 2019-07-04.
## Ueda K (2020). iNaturalist Research-grade Observations. iNaturalist.org. https://doi.org/10.15468/ab3s5x. Accessed via GBIF on 2019-07-04.
## Warning in citation(x): no date field in DESCRIPTION file of package 'occCite'
## Warning in citation(x): could not determine year for 'occCite' from package
## DESCRIPTION file
## Column `YEAR` contains character strings.
## No coercion to numeric applied.
## Species: Tetrapturus angustirostris
##
## Australian Museum (2020). 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.
## BARDE Julien N, Inventaire National du Patrimoine Naturel (2019). Programme Ecoscope: données d'observations des écosystèmes marins exploités (Réunion). Version 1.1. UMS PatriNat (OFB-CNRS-MNHN), Paris. https://doi.org/10.15468/elttrd. Accessed via GBIF on 2019-07-04.
## BARDE Julien N, Inventaire National du Patrimoine Naturel (2019). Programme Ecoscope: données d'observations des écosystèmes marins exploités. Version 1.1. UMS PatriNat (OFB-CNRS-MNHN), Paris. https://doi.org/10.15468/gdrknh. Accessed via GBIF on 2019-07-04.
## Catania D, Fong J (2020). CAS Ichthyology (ICH). Version 150.242. California Academy of Sciences. https://doi.org/10.15468/efh2ib. 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. (2020). rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.3.0. 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.
## Feeney R (2019). LACM Vertebrate Collection. Version 18.7. Natural History Museum of Los Angeles County. https://doi.org/10.15468/77rmwd. Accessed via GBIF on 2019-07-04.
## Frable B (2019). SIO Marine Vertebrate Collection. Version 1.7. Scripps Institution of Oceanography. https://doi.org/10.15468/ad1ovc. Accessed via GBIF on 2019-07-04.
## Harvard University M, Morris P J (2020). Museum of Comparative Zoology, Harvard University. Version 162.228. Museum of Comparative Zoology, Harvard University. https://doi.org/10.15468/p5rupv. Accessed via GBIF on 2019-07-04.
## Mackay K (2018). New Zealand research tagging database. Version 1.5. Southwestern Pacific Ocean Biogeographic Information System (OBIS) Node. https://doi.org/10.15468/i66xdm. Accessed via GBIF on 2019-07-04.
## National Museum of Nature and Science, Japan (2020). Fish specimens of Kagoshima University Museum. https://doi.org/10.15468/vcj3j8. Accessed via GBIF on 2019-07-04.
## Owens, H.L., Merow, C., Maitner, B., Kass, J.M., Barve, V., Guralnick, R.P. (NA). occCite: Querying and Managing Large Biodiversity Occurrence Datasets. R package version 0.3.0. https://github.com/hannahlowens/occCite.
## Queensland Museum (2020). Queensland Museum provider for OZCAM. https://doi.org/10.15468/lotsye. Accessed via GBIF on 2019-07-04.
## R Core Team. (2019). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
## Raiva R, Santana P (2019). Diversidade e ocorrência de peixes em Inhambane (2009-2017). Version 1.4. 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 (2019). Diversidade e ocorrência de peixes na Zambézia (2003-2016). National Institute of Fisheries Research (IIP) – Mozambique. https://doi.org/10.15468/mrz36h. Accessed via GBIF on 2019-07-04.
## Robins R (2020). UF FLMNH Ichthyology. Version 117.280. Florida Museum of Natural History. https://doi.org/10.15468/8mjsel. Accessed via GBIF on 2019-07-04.
## South Australian Museum (2020). South Australian Museum Australia provider for OZCAM. https://doi.org/10.15468/wz4rrh. Accessed via GBIF on 2019-07-04.
## The International Barcode of Life Consortium (2016). International Barcode of Life project (iBOL). https://doi.org/10.15468/inygc6. Accessed via GBIF on 2019-07-04.
## Uchifune Y, Yamamoto H (2020). Asia-Pacific Dataset. Version 1.33. National Museum of Nature and Science, Japan. https://doi.org/10.15468/vjeh1p. Accessed via GBIF on 2019-07-04.
## Ueda K (2020). iNaturalist Research-grade Observations. iNaturalist.org. https://doi.org/10.15468/ab3s5x. Accessed via GBIF on 2019-07-04.
## Western Australian Museum (2019). Western Australian Museum provider for OZCAM. https://doi.org/10.15468/5qt0dm. Accessed via GBIF on 2019-07-04.
## Warning in citation(x): no date field in DESCRIPTION file of package 'occCite'
## Warning in citation(x): could not determine year for 'occCite' from package
## DESCRIPTION file
## Column `YEAR` contains character strings.
## No coercion to numeric applied.
## Species: Tetrapturus belone
##
## Bentley A (2020). KUBI Ichthyology Collection. Version 17.67. University of Kansas Biodiversity Institute. https://doi.org/10.15468/mgjasg. Accessed via GBIF on 2019-07-04.
## Chamberlain, S., Barve, V., Mcglinn, D., Oldoni, D., Desmet, P., Geffert, L., Ram, K. (2020). rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.3.0. 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.
## Harvard University M, Morris P J (2020). Museum of Comparative Zoology, Harvard University. Version 162.228. Museum of Comparative Zoology, Harvard University. https://doi.org/10.15468/p5rupv. Accessed via GBIF on 2019-07-04.
## Owens, H.L., Merow, C., Maitner, B., Kass, J.M., Barve, V., Guralnick, R.P. (NA). occCite: Querying and Managing Large Biodiversity Occurrence Datasets. R package version 0.3.0. https://github.com/hannahlowens/occCite.
## R Core Team. (2019). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
## Ranz J (2017). 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.
## Robins R (2020). UF FLMNH Ichthyology. Version 117.280. Florida Museum of Natural History. https://doi.org/10.15468/8mjsel. Accessed via GBIF on 2019-07-04.
## UMS PatriNat (OFB-CNRS-MNHN), Paris (2020). Données d'occurrences Espèces issues de l'inventaire des ZNIEFF. Version 1.1. https://doi.org/10.15468/ikshke. Accessed via GBIF on 2019-07-04.
## Warning in citation(x): no date field in DESCRIPTION file of package 'occCite'
## Warning in citation(x): could not determine year for 'occCite' from package
## DESCRIPTION file
## Column `YEAR` contains character strings.
## No coercion to numeric applied.
## Species: Tetrapturus georgii
##
## Bentley A (2020). KUBI Ichthyology Collection. Version 17.67. University of Kansas Biodiversity Institute. https://doi.org/10.15468/mgjasg. Accessed via GBIF on 2019-07-04.
## Chamberlain, S., Barve, V., Mcglinn, D., Oldoni, D., Desmet, P., Geffert, L., Ram, K. (2020). rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.3.0. 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.
## Orrell T (2020). NMNH Extant Specimen Records. Version 1.35. National Museum of Natural History, Smithsonian Institution. https://doi.org/10.15468/hnhrg3. Accessed via GBIF on 2019-07-04.
## Owens, H.L., Merow, C., Maitner, B., Kass, J.M., Barve, V., Guralnick, R.P. (NA). occCite: Querying and Managing Large Biodiversity Occurrence Datasets. R package version 0.3.0. https://github.com/hannahlowens/occCite.
## R Core Team. (2019). 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 (2016). International Barcode of Life project (iBOL). https://doi.org/10.15468/inygc6. Accessed via GBIF on 2019-07-04.
## Warning in citation(x): no date field in DESCRIPTION file of package 'occCite'
## Warning in citation(x): could not determine year for 'occCite' from package
## DESCRIPTION file
## Column `YEAR` contains character strings.
## No coercion to numeric applied.
## 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.
## BARDE Julien N, Inventaire National du Patrimoine Naturel (2019). Programme Ecoscope: données d'observations des écosystèmes marins exploités (Réunion). Version 1.1. UMS PatriNat (OFB-CNRS-MNHN), Paris. https://doi.org/10.15468/elttrd. Accessed via GBIF on 2019-07-04.
## BARDE Julien N, Inventaire National du Patrimoine Naturel (2019). Programme Ecoscope: données d'observations des écosystèmes marins exploités. Version 1.1. UMS PatriNat (OFB-CNRS-MNHN), Paris. https://doi.org/10.15468/gdrknh. 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. (2020). rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.3.0. 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.
## Owens, H.L., Merow, C., Maitner, B., Kass, J.M., Barve, V., Guralnick, R.P. (NA). occCite: Querying and Managing Large Biodiversity Occurrence Datasets. R package version 0.3.0. https://github.com/hannahlowens/occCite.
## R Core Team. (2019). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
## Robins R (2020). UF FLMNH Ichthyology. Version 117.280. Florida Museum of Natural History. https://doi.org/10.15468/8mjsel. Accessed via GBIF on 2019-07-04.
## The International Barcode of Life Consortium (2016). International Barcode of Life project (iBOL). https://doi.org/10.15468/inygc6. Accessed via GBIF on 2019-07-04.
Visualization features
Search result summary figures
occCite includes a function called sumFig.occCite() which is capable
of generating three types of plots from an occCiteData object. First,
let’s see how the results of our multiple species query looks.
sumFig.occCite(myPhyOccCiteObject,
bySpecies = F,
plotTypes = c("yearHistogram", "source", "aggregator"))
We can also generate plots for each species in the occCiteData object
individually. Since GBIF is the only aggregator we used for the query,
I’ll skip generating the aggregator plot.
sumFig.occCite(myPhyOccCiteObject,
bySpecies = T,
plotTypes = c("yearHistogram", "source"))
Mapping occCite search results
occCite also contains tools to map search results. We can pass occCite
objects to be mapped as an interactive html widget using leaflet. If
there are multiple species in a single occCite object, occurrences will
be color-coded by species. If you click on a single occurrence, you will
be able to see the species, the coordinates, the date on which it was
collected, the dataset to which it belongs, and the dataservice that
provided the record.
map.occCite(mySimpleOccCiteObject, cluster = T)
