The Open Tree of Life’s education and outreach site

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A little known side element to the Open Tree of Life project is the “Edu Tree of Life,” an interactive educational experience to engage the public. Nearing completion, our goal with this website has been to educate young students as well as the general public on topics surrounding evolution and phylogenetic trees. Our approach is to visually inform and engage users with colorful and entertaining animation, interactive features, and contextualization of facts and figures.

Our educational site is composed of three unique, interactive views of the ToL:

1) A “Big Picture” tree provides a zoomed-out timeline perspective of life’s history on earth and explains key elements of the tree of life using a stylized, graphic visualization. This ‘macro’ view presents the evolutionary history of Earth, starting from the creation of our planet and spanning all the way to present day. As the user moves up the timeline, the tree ‘grows’ in front of them revealing historical information; each new screen also offers a detailed explanation of one of several core concepts surrounding evolution. Video explanations containing animations live narrators explain each of these core concepts. Along with the videos, ‘pop-up’ information boxes also offer information.

Key elements:

  • A macro View
  • Key Concepts
  • Timeline of Life
  • Chaptered Format/Parallax Scrolling
  • Videos and Animation

The core concepts we explore are:

  • The Origin of Life
  • The Three Domains of Life
  • Common Ancestors
  • Extinction
  • Biodiversity
  • Lateral/Horizontal Gene Transfer & Genes

2) The page titled ““Categorizing Life on Earth” is a mid-sized view of life, a data-driven interactive tree with a focus on the groupings of species (clades). This Tree uses a sampling of data to illustrate hierarchy with a familiar ‘tree’ structure that employs branching lines of evolution. It pulls images from Phylopic and data from EOL for descriptions. A user can expand and contract nodes to view clades they find interesting. Still to come: we are exploring ways to illustrate LGT and are working on connecting nodes back via their common ancestry, so that clicking any two nodes will show you a visualization of how those species are connected through the whole tree of life.

Key elements:

  • Mid-sized view of major clades
  • Data-driven interactive
  • Shows Common Ancestry, Phylogeny and Clades
  • Species groupings ending in Clades

3) The “Explore Species” page is our ‘micro view’ of species on Earth. This interactive spinning wheel allows a user to select any of about 180 species to learn about. The 180 species were chosen as exemplary based on many factors: some were chosen for their relative familiarity with the general public, but many were chosen due to specific scientific breakthroughs associated with them. Many were the first species within their field of study to be gene-sequenced, some are keystone species with important evolutionary relatives, and others have strange or unique characteristics worthy of mention.

The information offered for each species includes an image (when available), scientific and common names, the major domain within which the species resides, and then a brief description of the species. This was achieved using the Encyclopedia of Life’s online API, which allowed us to pull information and other resources off of their site to show on ours. As a way of opening an educational portal between the two, any species you click on in the Wheel of Life can also be visited on its parent page at the Encyclopedia of Life, where much more information about all species can be found. We hope that this partnership will prove very fruitful for bringing in casual interest and turning it into a burning passion for evolutionary science and history. Even if we only end up with a few more zoologists, we’ll be happy.

Key elements:

  • Micro View
  • Exemplary/representative species
  • Connects to EoL API, a gateway for further learning
  • Catalogue of interesting species.­­
  • Some info on Major Domains.
  • Fun, introductory look into species and their connections.

We welcome your comments. —John Allison and Karl Gude


This is the first in a series of posts about several  phylogeny initiatives newly-funded by NSF focused on both technical and community aspects of phylogeny.  Plenty of potential for mutually beneficial work with OpenTree, and we are excited to help.

First up… FuturePhy!

FuturePhy is an NSF-sponsored, three-year program of conferences, workshops and hackathons on the Tree of Life. The project aims to promote novel, integrative data analyses and visualization, interdisciplinary syntheses of phylogenetic sciences, and cross-cutting uses of phylogenetics to develop and address new research questions and applications.

The first phase of this mission is critical: to bring together a broad community of people from diverse backgrounds who are active in phylogenetics research, who use the tree of life in research or education, who will benefit in applied or practical ways from a comprehensive tree of life, or who come from a background that offers new perspectives on defining, addressing or transcending key challenges in phylogenetics.

Help accelerate progress in all aspects of phylogenetics research by joining FuturePhy today. Diverse opportunities will be available to attend FuturePhy sessions in person or virtually, and to link FuturePhy to existing projects and initiatives.

  1. We invite you to participate in the project in several ways:
    Register on Scientists from all aspects of the phylogenetic sciences, educators, members of the tree-using community, and others interested in phylogenetics are welcome.
  2. Take the community survey and let FuturePhy what workshop and hackathon topics they should fund.
  3. Contribute to the discussion forum on This is the best way to log your interest and contribute ideas.
  4. Send email at with ideas or comments
  5. Tweet to the FuturePhy community: @FuturePhy
  6. Comment in the FuturePhy phylobabble thread

Crandall Lab Update: What can we do with synthetic trees?

Currently, the Crandall Lab is examining ways to use the underlying OpenTree taxonomy to gather metadata, associate it with nodes and tips in our synthetic trees, and apply it to evolutionary studies. Below we discuss them in the context of ongoing projects in the lab.


Curated taxonomy

One of the major outcomes of the OpenTree project is the underlying taxonomy. A curated taxonomy allows us to search and align names across independent databases to pull out additional information to associate with node and tip names. The Crandall Lab taxonomy curation started with the freshwater crayfish, which are in the Infraorder Astacidea and includes 711 species spread among 7 families.   This was a great group to start with because of the limited number of species and there are only a few active systematists revising the alpha-taxonomy, which makes the literature less dense and easier to work with. Initially, our investigations deemed the taxonomy very accurate, but the main issue we had to contend with was spelling errors attributed to depositing sequences into GenBank. In all, we identified and removed 10 misspelled taxa. Although that seems small, it was a great warm-up for two larger groups we are now working on, the Decapoda (crabs, shrimps, lobsters) which includes ~15,000 species and the Hemiptera (true bugs) which includes ~ 50,000-80,000 species.


Using a curated taxonomy to obtain additional data

As mentioned above, once the names have been curated we can use them to search across databases. This has been extremely useful in obtaining additional metadata to associate with our synthetic trees. For example, the Crandall Lab recently published a synthetic tree of the crayfish (Fig. 1), which included IUCN Red List values plotted for those taxa with assigned values (Owen et al. 2015, Richman et al. 2015). This is only feasible because we are able to search across IUCN Red Listed crayfish species using the OpenTree curated taxonomy names.

Other applications of using a curated taxonomy to obtain metadata include searching across GenBank to identify whether a particular taxon or rank has molecular data associated with it. This is useful for determining sampling strategies for new and continuing studies. For example, using the OpenTree taxonomy to search GenBank for Hemiptera families and genera, we found a wealth of sequence data has been generated for most of the higher taxa. The most diverse suborder within Hemiptera is Heteroptera and our query of names against NCBI GenBank suggests 70 of the 83 described families within Heteroptera have sequence data for one or more of the traditional eight molecular loci used in Hemiptera systematics (Fig. 2A). As for the Hemiptera genera identified in GenBank, we are currently validating the numbers in Fig. 2B because Hemiptera alpha-taxonomy is very active because many species are vectors for human pathogens and agricultural pests (e.g., kissing bug, aphids, psyllids, etc.).


In addition to searching GenBank, we are currently associating geographic, morphological, and ecological metadata to our curated names through GBIF and EOL TraitBank. We believe the curated OpenTree taxonomies of these groups and the accumulation of metadata for taxa will surely add a new dimension to our evolutionary studies and allow us to expand the scope the questions we can answer.

Figure 1 Synthetic tree of crayfish with 20 source trees.  Family names noted on the edge of the synthetic tree.  Paraphyly of Cambaridae is not novel and needs to be addressed in a morphological revision.  Color blocks note the IUCN Redlist value.

Figure 1 Synthetic tree of crayfish with 20 source trees. Family names noted on the edge of the synthetic tree. Paraphyly of Cambaridae is not novel and needs to be addressed in a morphological revision. Color blocks note the IUCN Redlist value.

Figure 2 Histograms depicting number of sequences found on GenBank given OTT names. 2A) Hemiptera families within suborders with nucleotide sequence data on NCBI GenBank. 2B) Hemiptera genera within suborders with nucleotide sequence data on NCBI GenBank.

Figure 2 Histograms depicting number of sequences found on GenBank given OTT names. 2A) Hemiptera families within suborders with nucleotide sequence data on NCBI GenBank. 2B) Hemiptera genera within suborders with nucleotide sequence data on NCBI GenBank.

Keith Crandall is a professor and director of the Computational Biology Institute at George Washington University. 

Chris Owen is a post-doctoral researcher for the AVAToL grant.


Owen, C. L., Bracken-Grissom, H., Stern, D., & Crandall, K. A. (2015). A synthetic phylogeny of freshwater crayfish: insights for conservation.Philosophical Transactions of the Royal Society of London B: Biological Sciences370(1662), 20140009.

Richman, N. I., Böhm, M., Adams, S. B., Alvarez, F., Bergey, E. A., Bunn, J. J., … & Collen, B. (2015). Multiple drivers of decline in the global status of freshwater crayfish (Decapoda: Astacidea). Philosophical Transactions of the Royal Society B: Biological Sciences370(1662), 20140060.

Update on synthesis methods

The current Open Tree of Life synthesis methods are based on the Tree Alignment Graphs described by Smith et al 2013. The examples presented in that paper used much simpler datasets than the dataset that is used for draft tree synthesis by the Open Tree of Life (which contains hundreds of original source trees and the entire OTT taxonomy with over 2.3 million terminal taxa). To accommodate the goals of synthesis, some modifications were made to the methods presented in Smith et al 2013. The current version of the draft tree (v2, which is presented at as of February 2015 and described in a preprint on bioRxiv), was built using these modified methods. The changes to synthesis that were introduced since Smith et al 2013 are not well-described elsewhere, so we present them below in this document.

We are continually testing and improving the methods we use to develop synthesis trees, and through this process we have recently discovered some methodological properties of the modified TAG procedures that are undesirable for our synthesis goals. We are making progress toward fixing them for the next version of the draft tree, and there are details at the end of this post.

General background on the Open Tree of Life project and the draft tree

The overall goal of OpenTree is to summarize what is known about phylogenetic relationships in a transparent manner with a clear connection to analyses and the published studies that support different clades. Comprehensive coverage of published phylogenetic statements is a very long term goal which would require work from a large community of biologists. The short-term goal for the supertree presented on the tree browser is to summarize a small set of well-curated inputs in a clear manner.

Background on Tree Alignment Graph methods

The current synthesis method uses a Tree Alignment Graph (TAG), described in Smith et al 2013. We have been using TAGs because:

  • These graphs can provide a view on conflict and congruence among input trees.
  • TAG-based are computationally tractable on the scale which the open tree of life project operates (2.3 million tips on the tree, and hundreds of input trees).
  • TAG-based approaches provide a straightforward way to handle inputs in which tips of a tree are assigned to higher taxa (any taxon above the species level). It is fairly common for published phylogenies to have tips mapped at the genus level (or higher).
  • When coupled with expert knowledge in the form of ranking of input trees, TAG methods can produce a sensible summary of our (rather limited) input trees. At this point in the project, our data store does not contain a large number of trees sufficiently curated* to be included in the supertree operations.

* Sufficiently curated = 1. tips mapped to taxa in the Open Tree Taxonomy; 2. rooted as described in the publication; 3. ingroup noted. Incorrect rootings and assignments of tips to taxa can introduce a lot of noise in the estimate, so we have opted for careful vetting of input trees rather scraping together every estimate available. We are hopeful that community involvement in the curation will get us to a point of having enough input trees to allow more traditional supertree approaches to work well, so that we can present multiple estimates of the tree of life.

Methods used to produce the v2 draft tree

The open tree of life project has been alternating between phases where we (1) add more trees to our set of curated input trees, and then (2) generate new versions of the “synthetic” draft tree of life. Thus far two versions of the tree have been publicly posted to The process of generating a new public draft tree involves the creation and critical review of many unpublished draft trees in order to detect errors or problems with the process (which could be due to misspecified taxa in input trees, software bugs, etc.).

This process has led to a few modifications of the TAG procedure as it was described in the PLoS Comp. Bio. paper. These modifications have been made to our treemachine software, and they include:

  • In the original paper, conflict was assessed by whether there was conflicting overlap among the descendant taxa of the nodes, not the edes. The software that produced the v2 tree assessed conflict between edges of the graph by looking for conflict based on the taxon sets contributed by each tree. This change is referred to as the “relationship taxa” rule in this issue on GitHub).
  • The supertree operation moves from root to tips, and occasionally a species attaches to a node via a series of low ranking relationships. When all of these are rejected (due to conflict with higher ranking trees), the species would be absent in the full tree if we followed the original TAG description faithfully. Instead, the treemachine version for v2 tree reattached these taxa based on their taxonomy after sweeping over the full tree.
  • The “Partially overlapping taxon sets” section of the paper described a procedure for eliminating order-dependence of the input trees. We have recently discovered a case in which the structure of a TAG built according to those procedures would differ depending on the input order of the trees. We have implemented a new procedure that pre-processes all the input trees, which removes this order-dependence (code for the new procedure can be accessed in the find-mrcas-when-creating-nodes branch of the treemachine repo on github).
  • To increase the overlap between different input trees, an additional step was implemented in treemachine that mapped the tips of an input tree to deeper nodes in the taxonomy that they may have represented. This was done by determining the most inclusive taxon that a tip could belong to without including any other tips in the tree, and then mapping the tip to that taxon instead of the taxon actually specified for the tip in the input tree itself. For example if the only primate in a tree was Homo sapiens, but the tree contained other mammals from the taxon sister to Primates (in the taxonomy), then the Homo sapiens tip would be assigned to the taxon Primates.

Undesirable properties of the procedures used to produce v2

  • It was possible for edges to exist in the draft tree that were not supported by any of the input trees. There were a very small number (111) of such groups in the v2 tree; this GitHub issue discusses the issue more thoroughly. This is not an unusual property for a supertree method to have – in fact most supertree methods can produce such groups. And under some definitions of support (e.g. induced triples) these groupings would probably have had support in our input trees. However, not being able to link every branch in the supertree to an branch in at least one supporting branch in an input tree made the draft tree more difficult to understand. We are working on modifications to the procedure that do not produce these groupings.
  • There were 22 taxonomic groupings mislabeled in the supertree (see issue 154 for details) and the definition of support used to indicate when an input tree “supported” a particular edge in the synthesis could be counterintuitive in some cases. The current view of the tree reports an input tree in the “supported by” panel if the branch in the draft tree passes along an edge that is parallel to an edge contributed by that input tree. Because some of the included taxa may have been culled from the group and reattached in a position closer to the root, the input tree can be in conflict with a grouping but still be listed as supporting it (see issues 155 and 157).

The draft tree contains over 2 million tips and many hundreds of thousands of internal edges. Thus, the undesirable properties mentioned above affected less than 0.0001% of the draft tree v2. Nonetheless, we are in the process of developing fixes for these problems, which should further improve the interpretability as well as the biological accuracy of future versions.

Preprint: Synthesizing phylogeny and taxonomy into a comprehensive tree of life

We’ve just posted a preprint on bioRXiv of our submitted manuscript on how we are combining taxonomy and phylogeny into a comprehensive tree of life:

You can browse the complete tree at

Comments welcome (either here or on bioRXiv). Note that the authorship list is woefully incomplete – biorxiv only allows 20 authors in the submission process. Here is the complete list:

Stephen A. Smith, Karen A. Cranston, James F. Allman, Joseph W. Brown, Gordon Burleigh, Ruchi Chaudhary, Lyndon M. Coghill, Keith A. Crandall, Jiabin Deng, Bryan T. Drew, Romina Gazis, Karl Gude, David S. Hibbett, Cody Hinchliff, Laura A. Katz, H. Dail Laughinghouse IV, Emily Jane McTavish, Christopher L. Owen, Richard Ree, Jonathan A. Rees, Douglas E. Soltis, Tiffani Williams

Tree-for-All hackathon series: Taxon sampling, part 1 

Sampling taxa with Python and Perl scripts

This continues a series of posts featuring results from the recent “Tree-for-all” hackathon (Sept 15 to 19, 2014, U. Mich Ann Arbor) aimed at leveraging data resources of the Open Tree of Life project.  To read the whole series, go to the Introduction page.

More specifically, this is the first of two posts addressing the outputs of the “Sampling taxa” team, consisting of Nicky Nicolson (Kew Gardens), Kayce Bell (U. New Mexico), Andréa Matsunaga (U. Florida), Dilrini De Silva (U. Oxford), Jonathan Rees (OpenTree) and Arlin Stoltzfus (NIST).[1]

The “taxon sampling” idea

Although users seeking a tree may have a predetermined set of species in mind, often the user is focused on taxon T without having a prior list of species. For instance, the typical user interested in a tree of mammals does not really want the full tree of > 5000 known species of mammals, but some subset, e.g., a tree with a random subset of 100 species, or a tree of the 94 species with known genomes in NCBI, or a tree with one species for each of ~150 mammal families.

If we think about this more broadly, we can identify a number of different types of sampling, depending on what kinds of information we are using, and how we are using it. First, sampling T by sub-setting is simply getting all the species in T that satisfy some criterion, e.g., being on the IUCN red list of endangered species,  or having a genome entry in NCBI genomes, a species page in EOL, or an image in (organism silhouettes for adorning trees).

Second, we might use a kind of hierarchical taxonomic sampling to get 1 (or more) species from each genus (or family, order, etc.).


Poster from hackathon day 1, making the pitch for sampling taxa as a hackathon target

Third, we could reduce the complexity of a taxon or clade without using any outside information— what we might call down-sampling—, e.g., get a random sample of N species from taxon T, down-sample nodes according to subnode density, or choose N species to maximize phylogenetic diversity.

Finally, we can imagine a kind of relevance sampling, where we choose (from taxon T) the top N species based on some external measure of importance or relevance, e.g., the number of occurrence records in iDigBio (or GBIF, iNaturalist, etc.), the number of google hits (i.e., popular species), or the number of PubMed hits (i.e., biomedically relevant species).

Products of the “taxon sampling” team

At the tree-for-all hackathon, the “taxon sampling” team took on the challenge of demonstrating approaches to sampling from a taxon, making their products available in their github repo. The group focused its effort on creating multiple implementations for 3 specific use-cases:

  • sub-setting: get species in T with entries in NCBI genomes
  • down-sampling: get a random sample of N species from T
  • relevance sampling: get the N species in T with the most records in iDigBio

Each approach relies on 2 key OpenTree web services (described and illustrated in the introduction): the match_names service (click to read the docs) to match species names to OpenTree taxon identifiers (ottIds), and the induced_tree service to get a tree for species designated by these identifiers.

Here I’ll describe two projects based on command-line scripts in Python and Perl.  In the next post, I’ll describe how taxon sampling was implemented within an existing platform with a graphical user interface, including Open Refine (spreadsheets), PhyloJIVE, and Arbor.

Down-sampling in Python

A simple down-sampling approach via random choice is implemented in the script developed by Dilrini De Silva (Oxford) and Jonathan Rees (OpenTree), as in this example:

python -t Mammalia -m random -n 50 -o my_induced_tree.nwk

Here, “Mammalia” can be replaced by another taxon name, “50” may be replaced by another number, and the -o flag is used to specify an output file. The script calls on the OpenTree functions via the ‘opentreelib’ python library (another hackathon product available on github) to interact with OpenTree. It retrieves the unique OTTid of a higher taxon specified via the -t flag, and queries OpenTree to retrieve a subtree under that node. It parses the subtree to identify the implicated species, selects a random sample of the species, and requests the induced subtree, writing this to a newick file.
my_induced_subtree_example_mammalia copy
This script also invokes a rendering library to create a graphic image of the tree from the command-line, as in the example (figure) showing a random sample of 10 mammals.

Sub-setting in Perl

The specific sub-setting challenge that the team picked was to get a tree for those species (in a named taxon) that have a genome entry in NCBI genomes. NCBI offers a programmable web-services interface called “eutils” to access its databases. Because NCBI searches can be limited to a named taxon, it is possible to query the genomes database with the “esearch” service for “Mammalia” (or Carnivora, Reptilia, Carnivora, Felidae, Thermoprotei), cross-link to NCBI’s taxonomy database using the “elink” service, get the species names using the “esummary” service, and then use OpenTree services (as described in the Introduction) to match names and extract the induced tree.

This 5-step workflow, which illustrates the potential for chaining together web services to build useful tools, was implemented by Arlin Stoltzfus (NIST) as a set of Perl scripts. The master script invokes 5 other standalone scripts, one for each step. The last 2 scripts are simply command-line wrappers for OpenTree’s match_names and induced_subtree methods. All the scripts are available in the Perl subdirectory of the team’s github repo. They are demonstrated in the brief (<2 min) screencast below.


The taxon sampling group produced several other products.  In the next post, I’ll describe how taxon sampling was implemented within environments that provide a graphical user interface, including Open Refine (spreadsheets), PhyloJIVE (phylogeographic visualization), and Arbor (phylogeny workflows).

[1] The identification of any specific commercial products is for the purpose of specifying a protocol, and does not imply a recommendation or endorsement by the National Institute of Standards and Technology.


Why Do We Need Big Trees, Anyway?

An explicit goal of the Open Tree of Life is to create a single phylogenetic tree that encompasses all living (and some extinct) biodiversity on earth. A question some may have, especially non-scientists, is why do we need a tree like that, and what would we do with it? You can’t even see it all at once, right? The answer to this question, of course, is that with bigger and more resolved trees we can answer evolutionary questions on scales not previously possible.

Currently, postdocs from the labs of Doug Soltis (Univ. of Florida) and Stephen Smith (Univ. of Michigan) are collaborating on several projects within the plant world that leverage the power of big trees. Cody Hinchliff, a postdoc in the Smith lab, recently presented some of these findings during a standing room only presentation at the Botanical Society of America conference in Boise, Idaho, employing a tree with almost complete generic level sampling to unravel evolution and diversification of epiphytes across vascular plants. Perhaps most surprisingly, Hinchliff found that most epiphyte lineages are relatively young, suggesting that either the widespread success that epiphytes currently exhibit is a recent phenomenon, or that epiphytic lineages are relatively short lived and evolve opportunistically in response to large-scale climate fluctuations. This, and other associated findings, are novel and exciting discoveries, and are examples of the insights that can be gleaned by analyzing character data across a massive data set.

Other collaborative “big tree” projects involving the Soltis and Smith labs involve the evolution of the aquatic habit within land plants and the evolution of floral characters in the order Lamiales. These studies involve Hinchliff and Stephen Smith, Bryan Drew from the University of Nebraska at Kearney (formerly a postdoc with Doug Soltis) and Doug Soltis, and undergraduates from all three institutions. The aquatic evolution project is looking at how the re-colonization of aquatic plants is linked to lineage diversification and whether an aquatic habit is associated with other character or habitat traits. The focus of the Lamiales study is investigating what suites of floral characters may be responsible for the extraordinary evolutionary success of the lineage, which at 23,000 species comprise about 1/12th of all flowering plants.

The fact that studies of this magnitude are not only possible, but ongoing, is a testament to the utility of big trees. Because these trees are nearly complete in terms of genera, we can account for virtually all diversity across these clades. Sparse lineage sampling and hence unaccounted for diversity has previously been a hindrance when analyzing evolutionary trends that span the tree of life, but the time is approaching (or might be here already!) where the size of the phylogenies will not be the limiting factor in studying broad scale evolutionary questions. This exciting development leaves researchers more time to examine and ponder truly interesting questions that could not be addressed previously. This is the power that big trees give us, and this is one of the reasons we need big trees.

Chronogram showing epiphytic evolution within vascular plants. Epiphytic lineages are shown in orange, and likely branches of epiphytic origin are in red. Root of tree is ~485 million years old.

Chronogram showing epiphytic evolution within vascular plants. Epiphytic lineages are shown in orange, and likely branches of epiphytic origin are in red. Root of tree is ~485 million years old.

Doug Soltis is a distinguished professor at the University of Florida.
Bryan Drew was previously a post-doctoral researcher in the Soltis lab and is currently an assistant professor at the University of Nebraska-Kearney.


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