**Tags:** coronavirus, dating, humor

Liana Finck for Man Repeller draws out the new timeline. I’m a couple of decades removed from this timeline, but it doesn’t look very fun. I’m always down to plant some scallions though. [via swissmiss]

**Tags:** coronavirus, dating, humor

**Tags:** coronavirus, mask, New York Times, simulation, subway

If someone sneezes in a closed space, you hope that the area has good ventilation, because those sneeze particles are going to spread. The New York Times explains in the context of a subway train.

Wear a mask.

**Tags:** coronavirus, mask, New York Times, simulation, subway

We are hosting this session to allow for the types of conversations about programs and initiatives that would normally happen at the Annual ESA Meeting, but couldn’t this year because of the virtual format. Program Officers will not be presenting prepared material, so please come prepared to type your questions into the Q&A box and hear them answered. We will have Program Officers representing a variety of different programs and topics throughout the day. They can answer questions about these programs or discuss other topics you may want to raise. As always, feel free to reach us through email as well.

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**Tags:** NOAA, satellite imagery

You can basically hook up an antennae to your laptop and start receiving images from space. This DIY guide from Public Lab amazes me.

The NOAA satellites have inbuilt radio antennas that transmit the data collected by the AVHRR instrument on a frequency in the 137 MHz range. To minimise interference between satellites, each NOAA satellite transmits on a different frequency within the 137 MHz range.

[…]

Your antenna is a sensor. It catches electromagnetic waves and transforms them into an electrical current i.e. an electrical signal. All antennas are tuned to specific frequency ranges meaning that they receive or transmit these frequencies best. Most antennas are directional.

I need to try this.

**Tags:** NOAA, satellite imagery

In the first post of this series (Farris and Felsenstein), I introduced two matrices, a Farris Zone matrix and a Felsenstein Zone matrix, with the same set of tip taxa: three extant genera and three

The Farris Zone matrix provides a perfect signal. No matter which inference criterion one uses, one always gets the true tree. In such a case, the taxon sampling should be irrelevant; and it is. Any 5-taxon sub-tree correctly shows only splits found in the 6-taxon true tree — shown below are the actual most parsimonious trees (MPT) of each inference using the branch-and-bound algorithm.

Six most-parsimonious trees showing the topology of the true tree; trees are midpoint-rooted and have the same scale. Note: NJ/LS and ML would give the same result for this experiment. |

Consequently, for the perfect case, the SuperNetwork of the six 5-taxon trees is the 6-taxon true tree.

Z-closure SuperNetwork (Huson et al. 2004) of the 5-taxon MPTs generated with SplitsTree (walkthrough at the end of the post) depicting the true tree. |

Therefore, the simplest test to check for potential topological issues in any set of data is to sub-sample the taxa by sequentially pruning a single taxon, infer the resulting group of trees (which I will call minus-one trees), and then summarize this tree sample in the form of a SuperNetwork. If the data have no signal issues – and the inferred all-inclusive tree is unbiased – all minus-one trees will be congruent with the all-inclusive inferred tree. The resulting SuperNetwork will then be a tree matching the inferred all-inclusive tree.

On the other hand, if removing a single taxon has a significant effect on the inferred tree, then this either means you need this taxon to get the right tree

Real data matrices are far from perfect. Paleophylogenetic matrices, for instance, not only include

If we repeat the same minus-one experiment, but now use the Felsenstein Zone matrix, instead, we end up with something quite different. We get three most-parsimonious tree (MPT) solutions when eliminating the outgroup genus O

First row rooted with Z, all other trees mid-point rooted. All trees have the same scale. |

By pruning the long-branching genera A or B, even parsimony analysis gets the correct tree because we have eliminated the source of the long-branch attraction. Adding fossils to break down long branches can be effective (classic paper: Wiens 2005), but dropping long-branching tip taxa works just as well. Changing between a close outgroup (fossil Z) and a distant outgroup (fossil O) has little benefit here.

In this case, the resulting SuperNetwork of our 10 MPTs is not a tree but a network including alternative clades, wrong ones (orange), ie. not monophyletic, and correct ones (green) — ie. branches (internodes, bipartitions) reflecting the monophyletic lineages of the true tree.

Comprehensive Z-closure SuperNetwork of the 10 minus-one MPT inferred based on the Felsenstein Zone matrix. The network includes all split patterns found in the MPT sample. |

To give an example of how sequentially dropping one taxon works with real-world data, we'll use the exhaustive 700 character matrix for bird-related dinosaurs provided by Hartman et al. (2019).

With its total of 501 taxa (OTUs), the apparent rationale behind the matrix is that, by including as many taxa as possible, one gets the best-possible (parsimony) trees, irrespective of the signal quality provided by individual OTUs. However, the full matrix cannot be forced into a single-optimal parsimony tree, due to missing data (72% of the matrix' cells are undefined or ambiguous, ie. 255969 cells) and a scarcity of synapomorphies (in a Hennigian sense) — this is discussed in Hartman et al.; see also the related Q&A.

Here, in light of the computational effort and to avoid heuristics when searching the MPTs, we'll use a pruned sub-matrix. For our first experiment, we take 15 out of the 19 best-covered OTUs. Thus, OTU pairs / triplets that are much more similar to each other than to any other OTU, are reduced to the best-covered representative.

The 19-taxon matrix that I used in a previous post (Large morphomatrices – trivial signal) had only one most-parsimonious tree solution, showing only clades in agreement with current opinion, which assumes a largely staircase-like evolution from dinosaurs to modern birds (Tree of Life). In contrast to the full matrix, the 19-taxon matrix provided high support for most clades (method-independent), reflecting the number of scored traits. The extant taxa, representatives of modern birds (duck, turkey and ostrich, all edible), have many derived cgaracters, with the extinct bird genus

The optimal topologies for the 19 best-covered taxon matrix. Green, the single most-parsimonious tree. Clade names copied from Wikipedia/Tree of Life. |

The ML and NJ/LS (except for one branch) trees were topologically identical; each branch is supported by about 100 inferred changes. The signal from the matrix should be straightforward.

The tree-size weighted mean (default in SplitsTree) SuperNetwork, summarizing the result of an exhaustive branch-and-bound search using the 15-dropped-1-taxon matrices (each one resulting in a single optimal MPT) has a tree-like structure.

Conflicting clades are found in only two of the 15 inferred MPTs, being represented by short branches (their length in the other 14 trees is counted as zero).

Nonetheless, these conflicts received considerable character support. The frequency of a split in the minus-1 tree sample is irrelevant (see the A-B LBA problem discussed above — any tree including A and B showed the wrong clade). When summarizing our tree sample (especially when using MPTs), we should hence opt for a SuperNetwork, in which the edge lengths give the minimum branch lengths found in the MPT collection, ie. the edge length reflects the minimum length of the branch in all trees showing that branch.

Same SuperNetwork as above, but using the "Min" option instead of the default setting for computing edge lengths. |

Without

Even the most comprehensive, least gappy of paleophylogenetic matrices have substantial signal issues. If a tree inference is dependent on which OTUs are sampled, we cannot assume that we will automatically get better trees simply by including everything we have. Some OTUs (in our experiment:

Walk-through for computing Z-closure SuperNetworks (Huson et al. 2004) in SplitsTree (v. 4, since v. 5 is still not fully functional):

- Make sure the tree sample for reading is in Newick format, including branch-length information. The trees can be in a single file or multiple files.
- Start SplitsTree.
- To read in the tree sample:
- File > Open, if your trees are in one file;
- File > Tools > Load multiple trees, if your files (eg. minus-1 MPTs) are in different files.

- Go to Networks > SuperNetwork. Choose "Min" for "Edge Weight" in the pop-up analysis window for the first graph. You can also try out "Mean"/"Sum" (short, rare alternatives will be less prominent), "AverageRelative" (trade-off) or "None" (branch-lengths in the minus-one tree sample are ignored). When using simple tree samples (little topological variation, matrix with fairly stringent signals), a single run (default) suffices. Increasing the number (eg. to 100) ensures no branching pattern in the minus-one tree sample gets lost. For instance, for the Felsenstein Zone matrix, a single run will give you a SuperNetwork capturing the major conflicting aspects, while 100 runs will lead to a higher dimensional graph that includes the correct BD and AC clades as alternatives. If you like to view the overall best-fitting tree instead of a network, tick "SuperTree".

Hartman S, Mortimer M, Wahl WR, Lomax DR, Lippincott J, Lovelace DM (2019) A new paravian dinosaur from the Late Jurassic of North America supports a late acquisition of avian flight.

Huson DH, Dezulian T, Kloepper T, Steel MA (2004) Phylogenetic super-networks from partial trees.

Wiens JJ (2005) Can incomplete taxa rescue phylogenetic analyses from long-branch attraction?