GBIF.us

Webinar: The Sampling Event Horizon: Lessons from the Publishing Frontier (11:00–12:30 pm ET)

2026-10-13T00:00:00+00:00

Ready to take your biodiversity data beyond simple occurrence records?

Join the next GBIF North America Webinar to learn about how publishing biodiversity data as Sampling Events unlocks richer, more reusable datasets. Pioneers from the GBIF community share their experience crossing the Sampling Event Horizon: once your data crosses into Sampling Event structure, can you go back to simple occurrence records?

In this panel, leaders and stalwart adventurers from the GBIF Secretariat and GBIF North America share lessons learned: the gotchas that cost them time, the bottlenecks that slowed them down, and practical advice for planning new efforts or retrofitting legacy data into this structure.

Please come with questions! After short talks from each panelist, we’ll open up to a Q&A/panel discussion covering whatever the sampling event frontier throws at us.

GBIF North America Webinar: Improving Data Quality at the Source: A Data Quality Toolkit for Biocollections

2026-06-05T00:00:00+00:00

Ever wondered how you can improve your data quality? If you have, this GBIF North America webinar is for you!

Happening Soon! Click Here to Register to Join us on 11 June 2026 as we explore the following:

Biocollections data often suffer from data quality issues that arise from the complexities of the digitization and mobilization processes, and these data quality issues can prevent the data from reaching its full potential for research and educational impact. Some data quality issues are complex, others are relatively simple to identify and fix and can provide momentum for further improvements. In this webinar, we will discuss several categories of common data quality issues in biocollections datasets, and we will share a toolbox of data quality checks and fixes that can be easily deployed in collection management systems or spreadsheets.

Hope to see you there!

Missed it? We got you covered! View the recording and the materials here: https://www.gbif-north-america.org/dqwebinar

2026 GBIF Graduate Researchers Award: Call for Nominations

2026-05-01T00:00:00+00:00

The Global Biodiversity Information Facility (GBIF) has opened the call for nominations for the 2026 GBIF Graduate Researchers Award, an annual program recognizing exceptional graduate research that advances biodiversity science through the use of open data. Each year, GBIF honors two graduate students—one master’s and one PhD—whose work demonstrates innovative use of GBIF-mediated biodiversity data. Awardees are selected through an international review process coordinated by the GBIF Science Committee and each receive a €5,000 prize. Winners will be announced in advance of the 2026 GBIF Governing Board meeting.

Eligibility

Nominees must:

Be currently enrolled in a master’s or PhD program
Be either a U.S. citizen or enrolled at a U.S. institution
Conduct research that makes significant use of GBIF-mediated data

As with previous years, students may not apply directly. Nominations must be submitted through national GBIF nodes.

U.S. nominations (GBIF.US)

The U.S. node, GBIF.US, will coordinate the national selection process and submit up to two nominations to GBIF.

Students in the United States who wish to be considered for nomination should work with their advisors to prepare materials and contact GBIF.US by 15 May 2026 to express interest and receive submission instructions.

To be considered for the U.S. nomination, please contact Libby Ellwood: 📧eellwood@floridamuseum.ufl.edu

Because the U.S. node must review and select candidates ahead of the global deadline, internal deadlines will occur earlier than the GBIF submission deadline. Prospective nominees are strongly encouraged to reach out well in advance.

Nomination Materials

Nomination packages must be submitted in English and include:

A brief (≈200-word) summary of the research
A detailed project description (up to ~5 pages)
Curriculum vitae
A letter from the student’s supervisor confirming enrollment
One to three letters of support
Submissions should clearly demonstrate how GBIF-mediated data were used and how the research contributes to biodiversity knowledge, informatics, or conservation.

Timeline

U.S. nomination deadline: (set by GBIF.US; contact early for details)
Global deadline for submission to GBIF: 29 May 2026

About The Award

The GBIF Graduate Researchers Award supports the next generation of scientists working at the intersection of biodiversity science and data. By recognizing innovative research built on open, FAIR biodiversity data, the program highlights the value of global data sharing and its role in addressing pressing environmental challenges.

GBIF Graduate Researchers Award Call

2025-02-06T14:00:00+00:00

GBIF has announced its call for nominations for the 2025 GBIF Graduate Researchers Award. Share how you’re using open biodiversity data from the GBIF network in your graduate research—you could earn global recognition and €5,000! The deadline for US applications is May 27, 2025. This award is aimed at graduate students whose master’s and doctoral studies rely on GBIF-mediated data.

Important Details

The US node staff, with help from the US science community, will nominate one master’s and one PhD candidate to the GBIF Secretariat.
The GBIF Secretariat will announce winners prior to the 32nd meeting of the GBIF Governing Board (GB32), on 18-20 October 2025 in Bogotá, Colombia, and present their work at that meeting.

Eligibility

Candidates must provide a letter of support verifying their affiliation with a university graduate programme (as a currently enrolled master’s, PhD, or post-doctoral student) to be eligible for the award. In addition, candidates must be either:
- a) citizens of a country participating in GBIF or
- b) students at an institution located in a GBIF participant country.

Nominations can come from the GBIF delegation or node of either the candidate’s country of citizenship or the country of the candidate’s host institution.

For detailed instructions on submitting applications visit GBIF, or contact the GBIF-US Node Manager, Steve Formel, sformel@usgs.gov. We look forward to reviewing the fantastic submissions this year!

Food for Feedingback

2025-02-06T14:00:00+00:00

Have you ever come across data in GBIF or OBIS and wanted to alert the provider that some detail needed revising? Doing so is as easy as clicking a button! But it’s also the kind of button you might not see until someone points it out to you.

Our friends over at the SCAR Antarctic Biodiversity Portal were kind enough to point the buttons out, along with some excellent tips on providing useful feedback. You can read their post here: https://www.biodiversity.aq/post/2025/how-to-feedback-gbif-obis/

CARE-ing on Thanksgiving

2024-11-22T05:00:01+00:00

Note: Typically, we use these blog posts to highlight the biodiversity evidence served through GBIF. For example, the wild turkey, Meleagris gallopavo, has over two million occurrences on GBIF, and these records have helped us understand its natural history and biogeography (Corona-M, 2020). Below, we present a different aspect of biodiversity data that is equally important.

What is Thanksgiving?

On November 28th, the United States will celebrate Thanksgiving. Last year, President Biden reminded us why Americans celebrate Thanksgiving. He said,

“Throughout our country’s history, this season of reflection and giving thanks comes in good times and tough ones. Before there was a United States of America, the Pilgrims celebrated Thanksgiving in honor of their first successful harvest and the support and generosity of the Wampanoag people who made it possible. Amid the fierce battle for our Nation’s independence, General George Washington and his troops celebrated Thanksgiving on the way to Valley Forge. During the Civil War, President Abraham Lincoln proclaimed Thanksgiving a national holiday to honor the blessings of our country, even as he fought to preserve our Union. (The White House, 2023)”

However, for many, this is a holiday of mixed feelings stemming from the complicated history of the holiday (Smith, 2003), and the treatment of Indigenous Peoples by the United States (Bior, 2021). With that in mind, we would like to take this opportunity to highlight the CARE principles for Indigenous Data Governance (Carroll et al., 2020).

The CARE principles

The CARE principles for Indigenous Data Governance (Collective Benefit, Authority to Control, Responsibility, and Ethics) are a framework for addressing the people and purpose of data (Carroll et al., 2021). The Global Indigenous Data Alliance website lays out how the FAIR principles, which have guided open data and open science, fall short of engaging with the rights and interests of Indigenous Peoples, by ignoring power differentials and historical contexts. Some excellent papers have been written suggesting ways to integrate the CARE principles into our science (Jennings et al., 2023) and data repositories (O’Brien et al., 2024). But, the gap between understanding and meaningful implementation can be large; it will take all of us to #BeFAIRandCARE.

Graphic by the Global Indigenous Data Alliance. https://www.gida-global.org/care

Like much of the data management community, GBIF-US sees the value of the CARE principles and is working to increase our situational awareness and implementation of the CARE principles (O’Brien et al., 2024). Because of the complexity of our data provider community and the complicated history and context of data that are stewarded by museums and other institutions, we need your help to apply CARE to our work. If you have thoughts, questions, or proposals for how GBIF-US can implement the CARE principles in a meaningful way, please reach out to us at gbif-us@usgs.gov.

References

The White House, 2023. A Proclamation on Thanksgiving Day, 2023 [WWW Document]. The White House. URL https://www.whitehouse.gov/briefing-room/presidential-actions/2023/11/22/a-proclamation-on-thanksgiving-day-2023/ (accessed 11.15.24).
Corona-M, E., Cruz Silva, J.A., 2020. Modelling the prehistoric geographical distribution of the genus Meleagris. Quaternary International, The Archaeology of Human-Bird Interactions: Essays in Honour of Dale Serjeantson Part I 543, 8–15. https://doi.org/10.1016/j.quaint.2020.03.053
Smith, A.F., 2003. The First Thanksgiving. Gastronomica 3, 79–85. https://doi.org/10.1525/gfc.2003.3.4.79
Bior, A., 2021. The Mashpee Wampanoag want you to know the full history behind Thanksgiving. NPR. URL https://www.npr.org/2021/11/25/1059262045/the-mashpee-wampanoag-want-you-to-know-the-full-history-behind-thanksgiving (accessed 11.15.24)
Carroll, S.R., Garba, I., Figueroa-Rodríguez, O.L., Holbrook, J., Lovett, R., Materechera, S., Parsons, M., Raseroka, K., Rodriguez-Lonebear, D., Rowe, R., Sara, R., Walker, J.D., Anderson, J., Hudson, M., 2020. The CARE Principles for Indigenous Data Governance. Data Science Journal 19. https://doi.org/10.5334/dsj-2020-043
Carroll, S.R., Herczog, E., Hudson, M., Russell, K., Stall, S., 2021. Operationalizing the CARE and FAIR Principles for Indigenous data futures. Sci Data 8, 108. https://doi.org/10.1038/s41597-021-00892-0
Jennings, L., Anderson, T., Martinez, A., Sterling, R., Chavez, D.D., Garba, I., Hudson, M., Garrison, N.A., Carroll, S.R., 2023. Applying the ‘CARE Principles for Indigenous Data Governance’ to ecology and biodiversity research. Nat Ecol Evol. https://doi.org/10.1038/s41559-023-02161-2
O’Brien, M., Duerr, R., Taitingfong, R., Martinez, A., Vera, L., Jennings, L., Downs, R., Antognoli, E., ten Brink, T., Halmai, N. and David-Chavez, D., 2024. Earth Science Data Repositories: Implementing the CARE Principles. https://doi.org/10.5334/dsj-2024-037

Announcing Kelsey Huelsman as new staff

2024-08-15T05:00:01+00:00

We’re excited to welcome our new Bromery Intern, Kelsey Huelsman. Kelsey is joining us for the next year through an Oak Ridge Institute for Science and Education (ORISE) Research Participation Program.

Who is Kelsey?

Kelsey is a recent University of Virginia graduate, with a Ph.D. in Environmental Science. Her doctoral research used fine-scale hyperspectral remote sensing to examine spectral variability and invasive plant species detection in northwestern Virginia plant communities (Huelsman et al. 2023). As she embarks on her post-doctoral career, Kelsey plans to continue studying plant communities, their biodiversity, and remote sensing applications.

What will Kelsey be doing?

Kelsey will be working with the Species Observations Group in the Science Analytics and Synthesis (SAS) Biogeographic Science Branch, synthesizing patterns in unplanned vegetation occurrence data and planned vegetation surveys across space, time, and scientific studies. Using multiple vegetation datasets from Virginia and the Mid-Atlantic, including occurrences in the Global Biodiversity Information Facility (GBIF) and vegetation surveys conducted by the National Ecological Observatory Network (NEON) and the Forest Service’s Forest Inventory and Analysis (FIA), she will be assessing the impacts of sampling bias on measures of biodiversity, providing a foundation for future applications of GBIF data to assess and monitor national biodiversity. The codebase and documentation generated from this investigative process will provide a customizable workflow for additional geographic, taxonomic, and temporal applications.

Citations

Huelsman, Kelsey, et al. “Spectral variability in fine-scale drone-based imaging spectroscopy does not impede detection of target invasive plant species.” Frontiers in Remote Sensing 3 (2023): 1085808. https://doi.org/10.3389/frsen.2022.1085808

Data Publication: Pacific Coast Rocky Intertidal Surveys

2024-08-14T05:00:01+00:00

We are excited to share that the newest dataset from “MARINe” (Multi-Agency Rocky Intertidal Network) has been published! This new dataset, titled, “MARINe/PISCO: Intertidal: MARINe Coastal Biodiversity Surveys: Swath Surveys Summarized”, includes over 11,000 occurrences of Echinoderms, Molluscs, and Arthropods along the Pacific Coast of North America.

Special Sauce

This particular dataset is extra tasty because it is partly a product of the “Marine Biodiversity Data Mobilization Workshop 2024”, an annual workshop to facilitate mobilization of data to OBIS and GBIF. GBIF-US and OBIS-USA have co-organized and co-led this workshop for the past three years, and we’re always stoked to see a dataset cross the finish line!

But there’s more! This data came to fruition through the support of the Central & Northern California Ocean Observing System (CeNCOOS), the U.S. Integrated Ocean Observing System (IOOS), and the U.S. Marine Biodiversity Observation Network (MBON).

What is MARINe?

“MARINe” (Multi-Agency Rocky Intertidal Network) has now published four rocky intertidal long-term monitoring and biodiversity surveys to OBIS and GBIF! It is the largest and longest-running effort of its kind, spanning the Pacific Coast of North America, with some sites having been sampled for as much as 30 years. These surveys shed light on intertidal impacts caused by disturbances (e.g., El Nino events, oil spills) and detect shifts in community composition due to disease, species introductions, and climate change.

The four MARINe datasets have contributed to almost 23,000 download events from OBIS, about 17,000 download events from GBIF, and been cited by 31 peer-reviewed publications¹ since first being published.

The OBIS- and GBIF-mediated datasets can be accessed here:

MARINe Long-Term Monitoring Surveys: Sea Stars and Katharina: OBIS, GBIF
MARINe Long-Term Monitoring Surveys: Photo Plots and Transects Summarized: OBIS, GBIF
MARINe Coastal Biodiversity Surveys: Swath Surveys Summarized: OBIS, GBIF
MARINe Coastal Biodiversity Surveys: Point Contact Surveys Summarized: OBIS, GBIF

Footnotes

Not sure how GBIF citations and literature tracking works? See these two links to learn more:
- https://www.gbif.org/citation-guidelines
- https://www.gbif.org/literature-tracking

Citations

Multi-Agency Rocky Intertidal Network (MARINe), Partnership for Interdisciplinary Studies of Coastal Oceans (PISCO), Raimondi P (2024). MARINe/PISCO: Intertidal: MARINe Coastal Biodiversity Surveys: Swath Surveys Summarized. Version 1.3. United States Geological Survey. Samplingevent dataset. https://ipt-obis.gbif.us/resource?r=marine_cbs_swath_surveys&v=1.3

Kylie Hollis joins GBIF-US and OBIS-USA as USGS CSFP Fellow

2024-07-09T06:00:00+00:00

We’re excited to welcome our new USGS 2024 Cooperative Summer Fellow: Kylie Hollis! Kylie is joining us for the next three months through the Cooperative Summer Fellowship Program. Kylie is one of 61 fellows selected to work with the U.S. Geological Survey this summer through the CSFP.

Who is Kylie?

Kylie is a recent University of Florida graduate, with bachelors’ degrees in Marine Science and Environmental Science. During her time at the university, her research focused on optimal conditions for oyster larval settlement, as well nutrient pollution impacts on Spartina grass-mussel mutualisms in salt marshes. As she enters her post-grad career, Kylie hopes to get involved with research focusing on anthropogenic impacts on estuarine community ecology and conservation of these communities.

What will Kylie be doing?

Kylie will be helping mobilize data from the Demopoulos Lab at the Wetland Aquatic Research Center (WARC). The project, “Data Archaeology: Mobilizing a Treasure Trove of Deep-Sea Biodiversity data for Global Access and Use”, will increase the FAIRness and openness of the data by crosswalking data to the Darwin Core and EML standards, and publishing it to GBIF and OBIS. A secondary product will be the codebase, and documentation, which will serve as a cornerstone for additional datasets to be mobilized.

Querying OBIS, GBIF, and Fishbase with AphiaIDs

2024-06-10T05:00:01+00:00

Querying OBIS, GBIF, and Fishbase with AphiaIDs

Author

Stephen Formel

What?

I recently attended a meeting to discuss Species Distribution Models, or SDMs. The attendees emphasized the importance of GBIF, OBIS, and FishBase for informing SDMs. One of the challenges that was discussed was how to query taxa consistently and efficiently across these databases.

Here I demonstrate how to use AphiaIDs, the identifier used by the World Register of Marine Species (WoRMS), to query and link the data from OBIS, GBIF, and FishBase. All of the code below is written in R. And a big thank you to Yi-Ming Gan, from the SCAR Antarctic Biodiversity Portal (hosted by Institute of Natural Sciences), for helping me connect the dots!

The basic idea:

Query WoRMS to identify your AphiaID(s).
Query WoRMS to get the equivalent Fishbase ID(s).
Query GBIF to get the equivalent identifier(s) from the GBIF taxonomic backbone.
Query OBIS, GBIF, and Fishbase for your taxa of interest.

Why?

The data needed to answer a question can be distributed across multiple repositories. Often, the first step to answering a question is to coordinate the data. All of the aforementioned databases are working to resolve this challenge, but it will probably be some time before science completely figures out how to seamlessly serve all data all the time!

How?

We’ll look at mahi-mahi, AKA Dorado, (Coryphaena hippurus) in the EEZ of Puerto Rico.

By Unknown author - NOAA FishWatch, Public Domain, https://commons.wikimedia.org/w/index.php?curid=22626272

Get your R packages set up

You’ll need the packages listed in the code block below to be installed if you want to complete the analysis yourself. In some cases, I’ve pointed out which package a function comes from by calling it explicitly. For example, worrms::wm_name2id is calling the wm_name2id package from the worrms package. I only did this to make it clear where the function is coming from. You can also load these packages with the library function, like I show in the code block below.

Note that while most of the packages can be installed with the install.packages() function, the mregions2 package needs to be installed from Github. See here for instructions.

library(dplyr)
library(mregions2)
library(sf)
library(wk)
library(worrms)
library(httr2)
library(tidyr)
library(ggplot2)
library(rnaturalearth)
library(rfishbase)
library(robis)
library(rgbif)
library(stringr)
library(ggtext)

Step 1. Query WoRMS to identify your AphiaID(s).

You can search WoRMS through your web browser, using scientific or common names. Here is the landing page for Coryphaena hippurus:

https://www.marinespecies.org/aphia.php?p=taxdetails&id=126846

You can see the AphiaID at the end of the link: 126846. There is also an R package, worrms that can help execute this query:

AphiaID <- worrms::wm_name2id(name = "Coryphaena hippurus")

Note that we put the AphiaID into an object named AphiaID that we will use below.

Step 2. Query WoRMS to get the equivalent Fishbase ID(s).

Here I show two ways to query the WoRMS API for the fishbase identifier. The first is via the httr2 package The second is via the worrms package. If you’re not familiar with APIs and how they work, John Waller at GBIF wrote a blog post that’s a good place to start.

library(httr2)

fishbaseID <- request(base_url = 'https://www.marinespecies.org/rest/AphiaExternalIDByAphiaID/') %>% 
  req_url_path_append(AphiaID) %>%
  req_url_query(`type` = 'fishbase') %>% 
  req_perform() %>%
  resp_body_json() %>%
  unlist()

To do the same thing with the worrms package:

library(worrms)

fishbaseID <- wm_external(id = AphiaID, type = "fishbase")

Step 3. Query GBIF to get the equivalent identifier(s) from GBIF

GBIF aligns the data to a custom taxonomic database known as the GBIF taxonomic backbone. Although most AphiaIDs map to the backbone, I’m not 100% positive that all of them do. This dissonance is a known problem in the world of taxonomic databases and smart people from all over the world are working on removing this challenge.

If you only have one species, or aren’t feeling very code savvy, you can also view this information by putting the API call into a web browser:

https://api.gbif.org/v1/species?datasetKey=2d59e5db-57ad-41ff-97d6-11f5fb264527&sourceId=urn:lsid:marinespecies.org:taxname:126846

But if you are feeling code savvy:

library(httr2)

sourceId <- paste0('urn:lsid:marinespecies.org:taxname:', AphiaID)

response <- request(base_url = 'https://api.gbif.org/v1/species') %>% 
  req_url_query(`datasetKey` = '2d59e5db-57ad-41ff-97d6-11f5fb264527', 
                `sourceId` = 'urn:lsid:marinespecies.org:taxname:159222') %>% 
  req_perform() %>%
  resp_body_json()

GBIF_backboneID <- response$results[[1]]$nubKey

Step 4. Query OBIS, GBIF, and FishBase for your taxa of interest.

OBIS

Geometry

Before we can execute our query, we need to create the geometry for the area we wish to search. This is important because without a geographic shape (AKA geometry) we’d be searching for Mahi anywhere on the planet!

We’ll grab an outline shape of the Puerto Rico EEZ from MarineRegions.org using the package mregions2 and turn it into a convex hull to simplify the geometry being queried. Then we’ll turn it into a text string in a standard format, Well-Known Text (WKT), so it can be used in the API queries.

#Make convex hull of PR EEZ from MarineRegions.org.
PR_EEZ <- mregions2::gaz_geometry(x = 33179) %>% 
  sf::st_convex_hull() %>% 
  sf::st_as_text() %>% 
  wk::wkt() %>% 
  wk::wk_orient()

Note: The GBIF API reads clockwise WKT as a hole in geometry. But the sf package outputs clockwise WKT by default. The functions from the wk package are the easiest way I’ve found to reverse the winding order the WKT.

Let’s start with OBIS, since it aligns to WoRMS and therefore uses AphiaIDs.

library(robis)

obis_results <- robis::occurrence(taxonid = AphiaID, 
                  geometry = PR_EEZ)

Let’s take a moment to create the citations for the four datasets that contributed to this query, and for OBIS. We will publish these citatoins at the bottom of this post. We’ll follow the guidance in the OBIS Manual:

OBIS_metadata <- obis_results$dataset_id %>% unique() %>% 
  robis::dataset(datasetid = .)

#generate citations

OBIS_citations <- list()

for(i in 1:nrow(OBIS_metadata)){
  
OBIS_citations[[i]] <- robis::generate_citation(title = OBIS_metadata[i,]$title,
                         published = OBIS_metadata[i,]$published,
                         url = OBIS_metadata[i,]$url,
                         contacts = OBIS_metadata[i,]$contacts %>% as.data.frame())
  
} %>% unlist()

# Make citation for OBIS itself:

date_accessed <- Sys.Date()
query_title <- "Occurrence records of Coryphaena hippurus (Linnaeus, 1758) in the Puerto Rico EEZ "

OBIS_citation <- paste0("OBIS (2024) ", 
                       query_title, 
                       '(Available: Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. www.obis.org. Accessed:', 
                       date_accessed, 
                       ')'
                       )

Then GBIF…

GBIF data can be accessed through the API without obtaining a DOI, but the best practice is to mint a DOI once you are convinced that your query is final. If you continue to filter your results after downloading, you can always mint a derived dataset DOI.

library(rgbif)

# download without DOI, to explore the data.
gbif_results <- rgbif::occ_data(taxonKey = GBIF_backboneID,
                                geometry = PR_EEZ) %>%
  .[["data"]]

#download with DOI, so I can cite the data.

rgbif::occ_download(
  user = 'sformel',
  email = 'sformel@usgs.gov',
  pwd = rstudioapi::askForPassword(prompt = "GBIF Password"),
  pred_and(pred("taxonKey", GBIF_backboneID),
           pred("geometry", PR_EEZ)
  ))

The message returned by the above code includes a download key and shows the suggested citation. I can also find this information under the downloads linked to my profile on gbif.org. Create the citation to print below:

GBIF_citation <- rgbif::gbif_citation(x = occ_download_meta(key = '0197864-240321170329656'))[['download']]

…and finally FishBase:

Now let’s get some relevant trait info from Fishbase. FishBase has tons of information in it! So to make it a bit simpler, I’m only going to grab data about mahi-mahi reproduction.

library(rfishbase)

repro_table <- rfishbase::species_list(SpecCode = fishbaseID) %>%
  reproduction() %>%
  select(Species,
         ReproMode,
         Fertilization,
         Spawning, 
         RepGuild1,
         RepGuild2,
         ParentalCare)

Visualize Results

Let’s map the results, showing which data came from OBIS, which data came from OBIS, and displaying it alongside select reproductive traits from FishBase.

library(ggplot2)
library(rnaturalearth)

#Get an outline of Puerto Rico for mapping
PR <- rnaturalearth::ne_countries(country = 'Puerto Rico', 
                                  returnclass = 'sf', 
                                  scale = 'large')

# Select needed columns
obis_select <- obis_results %>% 
               select(occurrenceID, 
                      decimalLatitude, 
                      decimalLongitude) %>% 
  mutate(Source = 'OBIS')

gbif_select <- gbif_results %>% 
               select(occurrenceID, 
                      decimalLatitude, 
                      decimalLongitude) %>% 
  mutate(Source = 'GBIF')

# Join Data from GBIF and OBIS
mahi_joined <- rbind(obis_select,
                         gbif_select)

map_plot <- ggplot(PR) +
  geom_sf() +
  geom_point(data = mahi_joined,
             inherit.aes = FALSE,
             aes(x = decimalLongitude, 
                 y = decimalLatitude,
                 color = Source)) +
  
  #everything below here only serves to stylize the plot
  
  scale_color_manual(values = c('orange', 'skyblue')) +
  theme_bw(base_size=14) +
  theme(plot.title = ggtext::element_markdown(hjust = 0.5)) +
  guides(color = guide_legend(override.aes = list(size = 5))) +
  coord_sf(xlim = c(-64, -70)) +
  
  # This looks really complicated, but it's only style. Just making appropriate italics and line breaks.
  labs(title = "Map of _Coryphaena hippurus_ occurrences <br>in the Puerto Rico EEZ. <br>Data sourced from OBIS and GBIF.")

And finally, we print the plot and the table!

map_plot

repro_table %>% 
  tidyr::pivot_longer(cols = everything(),
                      names_to = "Term", 
                      values_to = "Value") %>% 
  knitr::kable(caption = "Select reproductive traits for <i>Coryphaena hippurus</i> from FishBase.")

Select reproductive traits for *Coryphaena hippurus* from FishBase.
Term	Value
Species	Coryphaena hippurus
ReproMode	dioecism
Fertilization	external
Spawning	Variable throughout range
RepGuild1	nonguarders
RepGuild2	open water/substratum egg scatterers
ParentalCare	none

Citations

Bakış, Y., Wang, X. FishNet2 Marine Data. Published 2023-12-19. https://fishnet.tulane.edu/ipt/resource?r=fishnet2_obis.
Benson, A., Diaz, G. NOAA Southeast Fisheries Science Center (SEFSC) Fisheries Log Book System (FLS) Commercial Pelagic Logbook Data. Published 2021-08-25. https://ipt-obis.gbif.us/resource?r=sefsc_logbook.
Garrison, L., Garrison, L., OBIS-SEAMAP. SEFSC Caribbean Survey 1995. Published 2021-09-08. http://ipt.env.duke.edu/resource?r=zd_11.
OBIS (2024) Occurrence records of Coryphaena hippurus (Linnaeus, 1758) in the Puerto Rico EEZ (Available: Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. www.obis.org. Accessed:2024-04-26)
GBIF Occurrence Download https://doi.org/10.15468/dl.8sjy3r Accessed from R via rgbif (https://github.com/ropensci/rgbif) on 2024-04-19

Session Info

This tells you the version and packages I had loaded as part of this blog creation.

sessionInfo() %>% print(locale = FALSE)

R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)

Matrix products: default


attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggtext_0.1.2        stringr_1.5.1       rgbif_3.7.9        
 [4] robis_2.11.3        rfishbase_4.1.2     rnaturalearth_0.3.4
 [7] ggplot2_3.5.0       tidyr_1.3.1         httr2_1.0.1        
[10] worrms_0.4.3        wk_0.8.0            sf_1.0-14          
[13] mregions2_1.0.0     dplyr_1.1.4        

loaded via a namespace (and not attached):
 [1] DBI_1.2.2                rlang_1.1.1              magrittr_2.0.3          
 [4] e1071_1.7-13             compiler_4.3.1           roxygen2_7.3.1          
 [7] vctrs_0.6.5              httpcode_0.3.0           pkgconfig_2.0.3         
[10] crayon_1.5.2             fastmap_1.1.1            backports_1.4.1         
[13] dbplyr_2.3.3             mapedit_0.6.0            ellipsis_0.3.2          
[16] utf8_1.2.4               promises_1.2.0.1         rmarkdown_2.24          
[19] markdown_1.7             tzdb_0.4.0               purrr_1.0.1             
[22] bit_4.0.5                rnaturalearthhires_0.2.1 xfun_0.43               
[25] cachem_1.0.8             jsonlite_1.8.8           progress_1.2.3          
[28] later_1.3.1              parallel_4.3.1           prettyunits_1.2.0       
[31] R6_2.5.1                 stringi_1.7.12           rdflib_0.2.8            
[34] Rcpp_1.0.11              knitr_1.46               triebeard_0.4.1         
[37] readr_2.1.4              httpuv_1.6.11            tidyselect_1.2.1        
[40] rstudioapi_0.15.0        yaml_2.3.8               curl_5.2.0              
[43] lattice_0.21-8           tibble_3.2.1             plyr_1.8.9              
[46] shiny_1.7.4.1            withr_3.0.0              askpass_1.2.0           
[49] evaluate_0.23            units_0.8-3              proxy_0.4-27            
[52] xml2_1.3.5               pillar_1.9.0             whisker_0.4.1           
[55] KernSmooth_2.23-22       checkmate_2.3.1          generics_0.1.3          
[58] vroom_1.6.5              sp_2.0-0                 hms_1.1.3               
[61] commonmark_1.9.1         munsell_0.5.1            scales_1.3.0            
[64] xtable_1.8-4             class_7.3-22             glue_1.6.2              
[67] lazyeval_0.2.2           tools_4.3.1              data.table_1.14.8       
[70] fs_1.6.3                 grid_4.3.1               contentid_0.0.17        
[73] crosstalk_1.2.0          urltools_1.7.3           colorspace_2.1-0        
[76] duckdb_0.8.1-1           cli_3.6.2                rappdirs_0.3.3          
[79] fansi_1.0.6              gtable_0.3.4             oai_0.4.0               
[82] digest_0.6.33            redland_1.0.17-18        classInt_0.4-9          
[85] crul_1.4.0               farver_2.1.1             htmlwidgets_1.6.2       
[88] memoise_2.0.1            htmltools_0.5.5          lifecycle_1.0.4         
[91] leaflet_2.2.0            httr_1.4.7               mime_0.12               
[94] gridtext_0.1.5           openssl_2.1.2            bit64_4.0.5