Eating Pudding

Poelen et al., 2014

Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. .

“The proof of the pudding is in the eating”, is a phrase that stuck out in detailed comments from Jan Willem Henfling on our recent paper (Poelen et al., 2014) in Ecological Informatics. With this, he pointed out that it is important to get the species interaction data into the hands of researchers and educators.

I was happy to read his comments, because it told me that our investment in writing and publishing an open-access paper (at a seemingly hefty price of $2500) is starting to pay off. Also, it highlighted that getting the interaction data out there for anyone to use is not enough: active collaborations are essential to show the use of our project. This is why I wanted to share some recent activities with you.

NESCent-EOL-BHL Research Sprint Feb. 4-7, 2014 (3) copy

Participants of NESCent-BHL-EOL Research Sprint on 4-7 February 2014 in Durham, North Carolina. Can you find the author?

After participating in the 4 day research sprint organized by NESCent, Biodiversity Heritage Library and Encyclopedia of Life at Durham, North Carolina in February 2014, I have been working with Brian Hayden to use GloBI data to show how dietary niche relates to biodiversity around the globe. Preliminary results are encouraging and a manuscript is in the works. Also, I have continued to work with Jen Hammock (Encyclopedia of Life, Smithsonian Institution) and Jim Simons (Gulf of Mexico Species Interactions, Texas A&M Corpus Christi) to put GloBI data to public use.

Tree-for-All hackathon participants gather to hear a progress report.

In September 2014, I participated in the week long Tree-for-All hackathon hosted at the University of Michigan and organized by Arbor Workflows and Open Tree of Life. Among many other things, this collaborative event helped create a method to retrieve phylogenetic trees related to species interactions (e.g. Pocket Gophers and Their Parasitic Chewing Lice) using rglobi (part of rOpenSci) and rotl R libraries.

In the time to come, I am looking forward to continue to help others eat (or make!) more of that delicious GloBI data pudding! Pudding anyone?

A Food-Web Map of the World

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A spatially integrated food web of the world derived from hundreds of thousands of interactions, across tens of thousands of species, and thousands of locations.

Sergey Slyusarev, Dimitrios-Georgios Kontopoulos, William Taysom, Adrian Guzman, and Bimlesh Wadhwa used GloBI data to create a food-web map as part of the Information Visualization MOOC class of 2014 at Indiana University. The map was created by combining interaction data from GloBI’s Darwin Core Archive with terrestrial and marine ecoregions of the world and various openly available taxonomies (e.g., ITIS, NCBI, WoRMS). After eliminating taxa with few recorded interactions, species with similar predator-prey characteristics were grouped by a custom algorithm that was inspired by the Jaccard index, a similarity measure, and based on Infomap, a community-detection algorithm. The resulting interconnected taxa communities were then used to make an information-packed (gorgeous!) food-web visualization. The map was generated with a combination of custom R scripts, existing libraries (e.g., igraph, Reol, rgdal), Cytoscape, and Adobe Illustrator.

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Explanation of how color, line width, and node size are used to encode spatial food-web information.

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Color encoding of ecoregions around the world, plotted with interaction locations.

I find the integration of spatial information (e.g., marine, terrestrial) in this graph useful because I can quickly relate specific interactions to regions in the world. For instance, I can easily spot a coastal interaction as a filled node that also has a colored border. In addition, the directionality of the interactions are easy to understand thanks to color coding: predator is orange, prey is blue. Opening the high-resolution image in a run-of-the-mill image viewer, I can easily browse the map by zooming and moving with touch-pad gestures. With the help of this visualization, data anomalies in GloBI’s complex data collection were detected, reported through GloBI’s issue list (see here, here, here, and here), and corrected. This alone tells me that the visualization by Slyusarev et al. is a useful research tool.

Special thanks to all GloBI data contributors, Sergey for his suggestions for improving GloBI, and Scott Weingart of Indiana University for inviting GloBI as a client project of IVMOOC 2014. Can’t wait to work with the IVMOOC class of 2015!

Exploring Antarctic Interactions Using GloBI’s Interaction Browser


Area selection tool in the GloBI’s Interaction Browser provides access to raw data files in addition to a share link. The “show” link updates the visualizations in other parts of the page.

Rugged scientists frequently brave the elements to study who eats what in those frigid yet productive waters of the Southern Ocean. Earlier this year, Ben Raymond was kind enough to share the Southern Ocean diet database that he developed with colleagues (Raymond et al. 2011) with GloBI. Having great data is one thing but . . . being able to (easily) explore the data is a challenge by itself. Enter Göran Bodenschatz, an enthusiastic, passionate web developer. Göran unleashed his skills to create a first pass at the GloBI’s Interaction Browser using d3, a javascript visualization library, in combination with the GloBI API. His html/ javascript source code is available here.

With Ben’s data and Göran’s tool, we can now “dial-up food webs” (phrase coined by Peter Roopnarine)  all across the Antarctic and discover that many species feast on Eurythenes gryllus and its cousin Eurythenes obesus. Not only are the interactions visualized on-the-fly using a dependency wheels, you can also access the raw csv, json or dot files to do offline analysis. In addition, you can share the selected area with others using a provided Interaction Browser “share” link.

This particular experience tells me that simply collecting and aggregating data is not enough. Only after locating and illuminating data with search and visualization tools,  I can start to analyze and perhaps understand the biological mechanisms behind the data hidden inside GloBI. . .


Screenshots of circular diagrams that highlight predatory interactions for Eurythenes gryllus around the Antarctic peninsula. The left diagram indicates the number of interactions by the width of the arc on the outside of the circle. The right diagram bundles the interactions to help detect highly interacting taxa. In the right diagram red indicates incoming interactions of selected taxon (e.g. prey), whereas green indicates outgoing interactions (e.g. predator).

What Parasites Does the Atlantic Croaker Host? Find Out on the Encyclopedia of Life

EOL's Atlantic Croaker species page with the GloBI data elements highlighted in pink.

EOL’s Atlantic croaker species page with the GloBI data elements highlighted in pink.

In the spring of 2013, a friend of mine pointed me to an article in the National Geographic about tongue-eating fish parasites. After suppressing my gag reflex upon seeing a picture of a parasite acting as a tongue of an Atlantic croaker (yes, the fish was still alive), I decided to request data from Colt W. Cook, author of a master’s thesis titled “The Early Life History and Reproductive Biology of Cymothoa excisa, a Marine Isopod Parasitizing Atlantic Croaker, (Micropogonias undulatus), along the Texas Coast.” Colt was kind enough to give me permission to add his dataset to GloBI.

Now that the Encyclopedia of Life has integrated GloBI data into its species pages, the Atlantic croaker page includes dietary habits as well as information about parasites such as Cymothoa excisa. It’s a win-win: users of the Encyclopedia of Life gain access to all sorts of structured species-interaction data, and the hardworking scientists who collected the data are attributed for their research.

Reference to Colt W. Cook dataset from the EOL's Atlantic Croaker data page.

Screenshot of the reference to Colt W. Cook’s thesis on the EOL Atlantic croaker data page.

At time of writing (January 24, 2014), GloBI includes about half a million global interactions with close to four hundred references, spanning over a century of species-interactions data. As GloBI continues to aggregate existing datasets, we lower the barrier to accessing important data and put the scientists who’ve made contributions to the field of biology in the spotlight.

Acknowledgments: big thanks to Colt W. Cook for sharing his data, and Jen Hammock and Patrick Leary for helping to integrate GloBI’s aggregated Darwin Core Archive into the Encyclopedia of Life.

Want to contribute data?

Want to access species-interaction data?

The Anatomy of GloBI


An anatomy of GloBI: Sources (datasets, taxonomies, ontologies) are aggregated into a single normalized metadata set. This dataset can be accessed in various ways to suit offline data analysis (Darwin Core), data integration (Linked Data), or interactive apps (JavaScript APIs or web services).

In the past year, we’ve written a bunch of software to normalize, aggregate, and expose various existing species-interaction datasets. To help understand the bits and pieces of the software that drives GloBI, I’ve included a system diagram in this post. You’ll find the data sources on the top (ontologies/datasets), the normalization magic in the middle, and the exports or APIs on the bottom. Also, the current (known) users (e.g., EOL pages and GoMexSI) are included. If you have an interest in learning more or sharing ideas on any of these topics, I invite you to read our wiki, play around with the JavaScript API, download aggregate datasets, comment on this post, or create a GitHub issue. It is your input that helps us to build the right things at the right time. Past feedback has led us to make the improvements we’re working on now. For instance, we are working on bettering the quality control of name mapping, providing examples for JavaScript APIs, and creating more mappings to existing ontologies, such as EnvO and Uberon, while extending our new interaction ontology.

Hoping to hear from you! Thank you for reading this post!

Want to Explore Marine Food Webs in the Gulf of Mexico? Visit GoMexSI

Ever wondered what happens beneath the surface of the Gulf of Mexico? Curious what a king mackerel (Scomberomorus cavalla) eats?  GoMexSI might help you find what you’re looking for.

GoMexSI_landing_pageGoMexSI (Gulf of Mexico Species Interactions) uses GloBI web services to provide the ability to explore marine predator-prey relationships in the Gulf of Mexico. GoMexSI offers three web tools for learning about trophic interactions in the Gulf of Mexico:


Taxonomic query for king mackerel (Scomberomorus cavalla) provides a diet breakdown, a list of interaction observations, and a link to download raw CSV data.

1) a taxonomic query tool that lists species-interaction observations with information, 2) a spatial query tool for finding species-interaction observations within a specific geographic area, and 3) a food-web explorer tool that helps to navigate visually through a food web.


Food-web explorer tool: species of interest in blue, predators in red, prey in green. You can click on predator or prey items to further explore the food web.

GoMexSI not only provides a valuable educational and research tool to study the food webs in the Gulf of Mexico, but also gives a great example of how GloBI web services can be used to recycle, repurpose, and liberate existing species-interaction datasets in our educational and scientific communities. The GoMexSI 1.0-beta version was released to the general public on September 3, 2013.

The GoMexSI website is developed by Reed Hewett and Michael Casavecchia under the guidance of James Simons of Texas A&M Corpus Christi and Jorrit Poelen (GloBI developer and author of this post). To learn more about the many other people and institutions that helped to get GoMexSI where it is today, visit

The continued success of GloBI and GoMexSI depends on data contributions of ecologist around the world. References to contributed datasets can be found at . If you’d like to share your species-interaction dataset, please open an issue at or send a message using

Ever Been Eaten by Flies in Puerto Rico? You’re Not Alone . . .

puerto_rico_interactions The graph above shows GloBI species interaction data in action, and answers a question you might ask yourself when the flies start nipping on your holiday in Puerto Rico.

The graph shows the interaction between source (top circles) and target (bottom circles) taxa or species. The lines between the taxa represent an observed interaction between the source (e.g., predator) and target (e.g., prey) organisms. In the graph, the top source taxon for true flies (Diptera) is selected, and, as you can see, they feast on all sorts of organisms, including us poor mammals (represented by the bottom light blue circles). The source and target circles are ordered in the same way and grouped according to their location in the tree of life or taxonomy. The colors of the circles represent the higher-order taxonomic rank (e.g., kingdom, phylum) of the taxon. Some people might call this interaction graph a bipartite graph, where others might see it as a simple form of a hive plot.

So by now, you should be able to answer the question you might ask when you visit Puerto Rico: “Am I the only one who’s getting eaten by flies?” This example shows how EOL’s GloBI enables easy access to detailed information about species interactions and helps answer very specific ecological questions.

If you are interested in accessing the data available through GloBI, please visit the GloBI data wiki. Also, if you’d like to build your own species-interaction visualization, check out the GloBI JavaScript visualization library. This npm-packaged library was used to generate the picture above. With your help and comments, we can improve the quality of GloBI data and its supporting libraries, so please do share your feedback.

Credits: Thanks to the friendly, inspiring folks at the Sudo Room‘s East Bay Javascript Meetup for providing input on the REST services and JavaScript visualization library. Special shout-outs to substackMax Ogden, and Bemson. Also, thanks to Ken-ichi Ueda of iNaturalist for sharing feedback on the interaction visualization.