• What was the discovery or innovation in this paper?
• How this was supported by evidence in the paper?
• What didn’t you understand about the paper, the methods, the questions, and/or the organisms?

I would use this info to tune what I covered in the intro for each class session. People brought a range of experience to the class, and I wanted to structure it so that everyone would learn a bit within each class session. Below are links to the posts for each week’s reading (slides, reflections on the paper, etc.):

• Tree building (Aug 22, 2025): Alison R. Irwin, Nicholas W. Roberts, Ellen E. Strong, Yasunori Kano, Daniel I. Speiser, Elizabeth M. Harper, and Suzanne T. Williams. 2025. “Evolution of Large Eyes in Stromboidea (Gastropoda): Impact of Photic Environment and Life History Traits” Systematic Biology 74(2):301–322. https://doi.org/10.1093/sysbio/syae063
• Bayesian analysis & fossilized birth death models (Aug 29, 2025): Laura P. A. Mulvey, Mark C. Nikolic, Bethany J. Allen, Tracy A. Heath and Rachel C. M. Warnock. 2025. “From fossils to phylogenies: exploring the integration of paleontological data into Bayesian phylogenetic inference” Paleobiology 51, 214–236. https://doi.org/10.1017/pab.2024.47
• Growth of the tree of life (Sep 05, 2025): Molly Chen, Artem I. Kholodov and Laura A. Hug (2025). “The evolution of the tree of life” Phil. Trans. R. Soc. B 380: 20240091. https://doi.org/10.1098/rstb.2024.0091
• Genomic species delimitation (Sep 12, 2025): Sonal Singhal, Adam D. Leaché, Matthew K. Fujita, Carlos Daniel Cadena, and Felipe Zapata. 2025. “A Genomic Perspective on Species Delimitation” Annu. Rev. Ecol. Evol. Syst. 2025. 56:467–89 https://doi.org/10.1146/annurev-ecolsys-102723-055311
• Phylogeography (Sep 17, 2025): Manica Balant¹, Daniel Vitales¹, Zhiqiang Wang¹, Zoltán Barina, Lin Fu, Tiangang Gao, Teresa Garnatje, Airy Gras, Muhammad Qasim Hayat, Marine Oganesian, Jaume Pellicer, Seyed A. Salami, Alexey P. Seregin, Nina Stepanyan-Gandilyan, Nusrat Sultana, Shagdar Tsooj, Magsar Urgamal, Joan Vallès, Robin van Velzen, Lisa Pokorny. 2025. “Integrating target capture with whole genome sequencing of recent and natural history collections to explain the phylogeography of wild-growing and cultivated Cannabis” Plants People Planet. 1-18. https://doi.org/10.1002/ppp3.70043 [¹ = equal contributions]
• Simulation (Sep 24, 2025): Ornela N. Dehayem, Ryan F. A. Brewer, Luis Valente, Frederic Lens, Rampal S. Etienne. 2025. “Impact of sampling strategy on inference of community assembly processes in phylogenetic island biogeography” Methods in Ecology and Evolution. 16:1507–1520. https://doi.org/10.1111/2041-210X.70058
• Behavior and genomics (Oct 3, 2025): Sara E. Lipshutz, Mark S. Hibbins, Alexandra B. Bentz, Aaron M. Buechlein, Tara A. Empson, Elizabeth M. George, Mark E. Hauber, Douglas B. Rusch, Wendy M. Schelsky, Quinn K. Thomas, Samuel J. Torneo, Abbigail M. Turner, Sarah E. Wolf, Mary J. Woodruff, Matthew W. Hahn & Kimberly A. Rosvall. 2025. “Repeated behavioural evolution is associated with convergence of gene expression in cavity-nesting songbirds” Nature Ecology & Evolution. https://doi.org/10.1038/s41559-025-02675-x
• Reticulate evolution (Oct 10, 2025): Gil Yardeni, Michael H. J. Barfuss, Walter Till, Matthew R. Thornton, Clara Groot Crego, Christian Lexer, Thibault Leroy and Ovidiu Paun. 2025. “The Explosive Radiation of the Neotropical Tillandsia Subgenus Tillandsia (Bromeliaceae) Has Been Accompanied by Pervasive Hybridization” Systematic Biology https://doi.org/10.1093/sysbio/syaf039
• Trait evolution (Oct 17, 2025): Verónica A. Rincón-Rubio, Rosana Zenil-Ferguson, Alejandro Gonzalez-Voyer. 2025. “The macroevolutionary consequences of the association between frugivory and carotenoid-dependent plumage coloration in passerine birds” Evolution, 2025, 79(8), 1643–1657 https://doi.org/10.1093/evolut/qpaf105
• Handling data deficiency in SDMs (Oct 24, 2025): Shubhi Sharma, Kevin Winner, Laura J. Pollock, James T. Thorson, Jussi Mäkinen, Cory Merow, Eric J. Pedersen, Kalkidan F. Chefira, Julia M. Portmann, Fabiola Iannarilli, Sara Beery, Riccardo De Lutio, Walter Jetz, 2025. “No species left behind: borrowing strength to map data-deficient species” Trends in Ecology & Evolution 40, 699–711. https://doi.org/10.1016/j.tree.2025.04.010
• Machine learning for traits (Oct 31, 2025): Roberta Hunt, José L. Reyes-Hernández, Josh Jenkins Shaw, Alexey Solodovnikov, Kim Steenstrup Pedersen. 2025. “Integrating Deep Learning Derived Morphological Traits and Molecular Data for Total-Evidence Phylogenetics.” Systematic Biology 74(3): 453-468 https://doi.org/10.1093/sysbio/syae072
• Alpha taxonomy and UCEs (Nov 7, 2025): Emma E. Jochim, James Starrett, Hanna R. Briggs, Jason E. Bond. 2025. “Speciation Pattern and Process in the California Coastal Dune Endemic Trapdoor Spider Aptostichus simus (Mygalomorphae: Euctenizidae) and Description of a New Cryptic Species” Ecology and Evolution 15:e72346 https://doi.org/10.1002/ece3.72346
• Multivariate traits (Nov 14, 2025): Emma Sherratt, Jenna Crowe-Riddell, Alessandro Palci, Ammresh, Mark N. Hutchinson, Michael S.Y. Lee, Kate L. Sanders. 2025. “Rapid evolution and cranial morphospace expansion during the terrestrial to marine transition in elapid snakes” Evolution 1–13 https://doi.org/10.1093/evolut/qpaf180
• Phylogenomics, recombination desert, and speciation (Nov 21, 2025): Nicole M. Foley, Richard G. Rasulis, Zoya Wani, Mayra N. Mendoza Cerna, Henrique V. Figueiró, Klaus Peter Koepfli, Terje Raudsepp & William J. Murphy. 2025. “An ancient recombination desert is a speciation supergene in placental mammals” Nature https://doi.org/10.1038/s41586-025-09740-2

Each week I’d decide on a topic, then look on OpenAlex, Google Scholar, and sometimes various journal websites for recent (only 2025) papers relevant to the topic. I would then read through these to find a paper that I thought would be useful for teaching, including whether I thought it was a good example of what to do (all papers have compromises, but I think students learn more from “good” papers than from ripping apart “bad” papers). I chose not to limit the papers only to ones in DAFNEE journals or only papers that are open access. There are issues with the publishing ecosystem, but for this class using that to limit papers would have resulted in too small a pool of papers this semester.

To subscribe, go to https://brianomeara.info/blog.xml in an RSS reader.

@online{o'meara2025,
  author = {O’Meara, Brian},
  title = {PhyloPapers 2025, {Summary}},
  date = {2025-12-01},
  url = {https://brianomeara.info/posts/phylopapers_2025_summary/},
  langid = {en}
}

Nicole M. Foley, Richard G. Rasulis, Zoya Wani, Mayra N. Mendoza Cerna, Henrique V. Figueiró, Klaus Peter Koepfli, Terje Raudsepp & William J. Murphy. 2025. “An ancient recombination desert is a speciation supergene in placental mammals” Nature https://doi.org/10.1038/s41586-025-09740-2

This paper used a deep learning approach (from way back in 2020! Adrion, Galloway, & Kern 2020) to estimate recombination rate along multiple mammal species’ genomes. It also aligned chromosomes to each other to show how overall structure remained fairly stable across evolutionary time.

For teaching, this was useful at showing the utility of using sliding windows when scanning along a genome, deep learning for parameter estimation, and looking at conflicts between gene tree and species tree topologies and the frequencies of the different possible gene trees. This paper was also nice for showing the impact of very data-rich graphics that still communicate a clear message. The potential impact of genes on the X chromosome for speciation is also something worth investigating more.

One reason I was excited to teach a class like this was to help my learning, which happened every week. The fun aspect of that in this paper was the conclusion that to understand phylogeny in tricky situations (such as ongoing gene flow) it could be better to focus on the non-recombining core of the X chromosome. I remember the days when we all used just mtDNA, then maybe a few nuclear genes, now various ways to sample across the entire genome. Going back to effectively a single history is counterintuitive, but after reading this paper it makes sense. I could see a use case for development of recombination-rate-aware gene tree - species tree approaches: use many genes and accommodate the realities of different gene histories, but with weighting so that areas with lower recombination rates have a greater weight in the final reconstruction. One could do a fast, dirty approach where one assigns weight to different genes (such as genes on the X chromosome having w times the weight of others, and do a sensitivity analyis of w) but if recombination is inferred as part of the analysis the impact would flow in naturally from the model.

I made intro slides with some of my background material and some figures from the paper: PDF and PowerPoint.

To subscribe, go to https://brianomeara.info/blog.xml in an RSS reader.

@online{o'meara2025,
  author = {O’Meara, Brian},
  title = {PhyloPapers 2025, {Phylogenomics}},
  date = {2025-11-21},
  url = {https://brianomeara.info/posts/phylopapers_2025_Nov_21/},
  langid = {en}
}

Emma Sherratt, Jenna Crowe-Riddell, Alessandro Palci, Ammresh, Mark N. Hutchinson, Michael S.Y. Lee, Kate L. Sanders. 2025. “Rapid evolution and cranial morphospace expansion during the terrestrial to marine transition in elapid snakes” Evolution, 2025, 0(0), 1–13 https://doi.org/10.1093/evolut/qpaf180.

The paper looks at skull evolution changes as snakes went back to the sea. It is a great, modern take on how geometric morphometrics works, using X-ray microcomputed tomography of skulls. It uses landmarks to measure shape and a variety of approaches to visualize changes and to estimate rates of change. It is also an opportunity to introduce students to the Procrustes myth (namesake of a statistical method).

One especially nice feature of this paper was its use of simulations – always a good way to get a sense of what the fitted models are actually saying about the morphology.

A necessary caveat when looking at spectacular evolutionary one-offs is that they are just that – N=1 examples. In this case, the group radiated into many species (and there’s also a different origin of a semi-aquatic species) but no matter how rigorous the statistics used are, it’s still a single origin of the change and the putatively correlated trait. The fully marine species do have a different shape and faster rate of evolution leading to them, but it could be due to something else changing on that branch besides the habitat shift. For example, if there were sexual selection for a sleeker head in that lineage, we would not know that led to the shift in shape rather than habitat – maybe the head shape shift unlocked the ability to be marine. This isn’t a criticism of this paper, but rather something everyone working on single synapomorphies must wrestle with, whether it’s the consequences of evolving a flower, vertebrate transition to land, or beaks in birds. It’s still possible to reject some hypotheses – for example, this paper had enough power that had it shown there was no association of the marine shift with head shape change, I would believe it, so showing there is an association present does give more weight to the idea that one change led to the other. It’s not the same as finding the same head shape change in five different cases when snakes went back to the ocean, but we just don’t have five such examples.

Though it wasn’t a focus of this paper, its mention of earlier work showing a decoupling of rates between speciation and morphology was also a benefit. We often have what I call the Martha Steward macroevolutionary hypothesis: “It’s a good thing” [the catchphrase of this American TV and cookbook personality]. We assume that some presumably adaptive trait will lead to increased speciation, lower extinction, and faster rates of evolution overall, even when there is no clear mechanism as to why (for example, perhaps living in the ocean results in fewer mating barriers and so should slow the rate of species formation). Work like that in this group shows that these rates can in fact operate independently of each other (and that rate shifts that are detected aren’t driven by confounding factors, like branches that are estimated to be too short in one clade that then results in too high rate estimates).

I made intro slides with some of my background material, especially about Ornstein-Uhlenbeck models, and some figures from the paper: PDF and PowerPoint. Sample figure from the slides, showing how OU models work (sigma is the amount of sugar the kid has been fed, alpha is the strength of pull of his cord, and theta is where he is being pulled to):

To subscribe, go to https://brianomeara.info/blog.xml in an RSS reader.

@online{o'meara2025,
  author = {O’Meara, Brian},
  title = {PhyloPapers 2025, {Multivariate} Traits},
  date = {2025-11-14},
  url = {https://brianomeara.info/posts/phylopapers_2025_Nov_14/},
  langid = {en}
}

Emma E. Jochim, James Starrett, Hanna R. Briggs, Jason E. Bond. 2025. “Speciation Pattern and Process in the California Coastal Dune Endemic Trapdoor Spider Aptostichus simus (Mygalomorphae: Euctenizidae) and Description of a New Cryptic Species” Ecology and Evolution 15:e72346 https://doi.org/10.1002/ece3.72346.

This revisits a clade of trapdoor spiders in California. It is noteworthy that even in a place with as many resources and researchers as California, and with organisms this charismatic, there remains open questions in taxonomy.

This paper used ultraconserved elements, UCEs, which have been popular recently as they have a mix of rates, so in theory some are useful for the questions being studied (and they come from across the genome):

Students engaged well with this paper, asking good questions about sampling, amount of evidence for the species boundaries, and the various methods used.

One thing that really impressed me about this paper was that it included the actual alpha taxonomy. I develop species delimitation methods, and so many published uses are basically “look, there are undescribed species here” and then the paper stops. This paper formally describes the new species: diagnostic characters, types, the works. Species boundaries are hypotheses, and I could imagine ongoing discussions about whether this group is under- or oversplit, but actually going from model conclusions to updating the taxonomy is a great step.

The paper also spurred good discussions about the practice of naming in taxonomy. The new species is named after Dr. Martina G. Ramirez, who has worked on spiders for decades, and who sounds like an amazing mentor for her students, so this naming avoids many of the issues of naming after people in the past with problematic impacts (see my SSB presidential address on this and related issues). However, naming organisms after people is very much an open question in our field, with reasonable advocates on many sides, so it was useful for students to work through the arguments in class to help understand the practice of taxonomy better.

I made intro slides with some of my background material and some figures from the paper: PDF and PowerPoint.

To subscribe, go to https://brianomeara.info/blog.xml in an RSS reader.

@online{o'meara2025,
  author = {O’Meara, Brian},
  title = {PhyloPapers 2025, {Alpha} Taxonomy},
  date = {2025-11-07},
  url = {https://brianomeara.info/posts/phylopapers_2025_Nov_7/},
  langid = {en}
}

Here we present Kosmos, an AI scientist that automates data-driven discovery. Given an open-ended objective and a dataset, Kosmos runs for up to 12 hours performing cycles of parallel data analysis, literature search, and hypothesis generation before synthesizing discoveries into scientific reports. Unlike prior systems, Kosmos uses a structured world model to share information between a data analysis agent and a literature search agent. The world model enables Kosmos to coherently pursue the specified objective over 200 agent rollouts, collectively executing an average of 42,000 lines of code and reading 1,500 papers per run. Kosmos cites all statements in its reports with code or primary literature, ensuring its reasoning is traceable. Independent scientists found 79.4% of statements in Kosmos reports to be accurate, and collaborators reported that a single 20-cycle Kosmos run performed the equivalent of 6 months of their own research time on average.

I’m not planning to use this (my accuracy is better than a C+, I’d hope), but I imagine colleagues will, and this is just its first generation. And researchers and others are using ChatGPT and related approaches to help with papers already, despite the known issues with made up citations. Popular app Grammarly now offers a service that lets users write scientific statements (their example is from geology) and then it automatically finds and cites relevant papers – no need to actually read the literature, just decorate your text with the funny names and numbers in parentheses that make it look credible. Grammarly claims it is both a “partner for better grades” and will help you “turn claims into supported arguments.”

There’s a whole discussion to have about the ethics of using such services and the wisdom of the many colleges paying for their communities to have access to them. But I want to talk about something different: which papers these services will choose.

Search Engine Optimization (SEO) has long been a concern for website owners: how do we make it so that when people search “vacation in Aruba” my hotel site is the top hit? That matters for people in science, too (people on the job market, make sure you have a good website!), but it’s less of an emphasis. For writing papers, there’s some optimization (like the endless debate over the wisdom of including colons in titles), but it’s a messy, human process. And journals have various tactics for bumping up their impact factor (“Hey, want to publish version 1.2.1 of your popular software package in our journal?”; “Has there been a really good review of this popular subject published this week? Let’s commission one and get those citations!”). But increasingly, the entities “reading” our papers and deciding what to cite won’t be humans: they’ll be AI recommendation engines telling people what is most relevant. Even once the rumored AI economic bubble bursts, I suspect it’s unlikely tools like this will go away, unless there becomes enough of a pushback on their use that it becomes unprofitable to provide them.

@online{o'meara2025,
  author = {O’Meara, Brian},
  title = {AI Optimization},
  date = {2025-11-07},
  url = {https://brianomeara.info/posts/ai_optimization/},
  langid = {en}
}

Roberta Hunt, José L. Reyes-Hernández, Josh Jenkins Shaw, Alexey Solodovnikov, Kim Steenstrup Pedersen. 2025. “Integrating Deep Learning Derived Morphological Traits and Molecular Data for Total-Evidence Phylogenetics.” Systematic Biology 74(3): 453-468 https://doi.org/10.1093/sysbio/syae072

Morphological traits have a long history in phylogenetics, and for some questions, like for reconstructions of long-dead species, they can be the only data available. This paper takes advantage of deep learning and a set of reference images for beetles to identify traits to use for phylogenetic reconstruction.

One useful bit of background was this introduction to convolutional neural networks (original page; archived). Note that “AI” covers a whole set of technologies, from ways to get text-based responses trained from a large corpus of text (“cheatGPT, write a five paragraph essay on the use of imagery in The Scarlet Letter”) to generating pictures or videos, to matching images or sounds to species (as in iNaturalist or Merlin); convolutional neural networks (CNN) are common ways to extract features and other information from images.

I was expecting this paper to extract discrete characters, as that are what are used most commonly in phylogenetics for tree inference: things like presence or absence of “pygidium exposed” or “geniculate antennae.” Instead it used continuous traits. Those are commonly ratios or other concrete trait measurements: head width divided by length, angle between two elements, etc. This uses features that the machine learning approach discovered but which are harder to map back to traits humans identify; for example, here’s which pixels matter to one of the traits used:

This paper shows these traits are informative and useful for recovering the phylogeny. It does not shy away from potential disadvantages, either, including the work still required and even the environmental cost of running these models.

Another thing this paper does excellently is providing supplementary data:

The data underlying this article are available at <http://doi.org/10.17894/ucph.39619bba‐4569‐4415‐9f25‐d6a0ff6 4f0e3> for the Rove‐Tree‐11 dataset and in the article’s dryad repository (<https://doi.org/10.5061/dryad. 9cnp5hqqq>) for the further molecular data and associated genbank accession numbers, example inference code, all generated trees, and stratified dataset split. All trained model runs and extracted trait matrices are available in the following erda repository https://erda.ku.dk/archives/440063cabdb1789ad82f31366c926b4e/published‐archive.html. The reference tree, best molecular tree and best total‐evidence tree can be found on TreeBASE at http://purl.org/phylo/treebase/phylows/study/TB2:S31300?x‐access‐code=397cc12bd8047bf52b312b4743f23e2b&format=html. The code used in this analysis is available on github https://github.com/robertahunt/Revisiting_Deep_Metric_Learning_PyTorch, commit a6654453c3b7785a17511255e02c468c53fe6f5d, forked from Roth et al. (2020).

It even includes putting the trees on TreeBase, something few in our field do despite the benefits to all (and citation bump for people who share).

I made intro slides with some of my background material and some figures from the paper: PDF and PowerPoint.

To subscribe, go to https://brianomeara.info/blog.xml in an RSS reader.

@online{o'meara2025,
  author = {O’Meara, Brian},
  title = {PhyloPapers 2025, {Machine} Learning for Traits},
  date = {2025-10-31},
  url = {https://brianomeara.info/posts/phylopapers_2025_Oct_31/},
  langid = {en}
}

Shubhi Sharma, Kevin Winner, Laura J. Pollock, James T. Thorson, Jussi Mäkinen, Cory Merow, Eric J. Pedersen, Kalkidan F. Chefira, Julia M. Portmann, Fabiola Iannarilli, Sara Beery, Riccardo De Lutio, Walter Jetz, 2025. No species left behind: borrowing strength to map data-deficient species. Trends in Ecology & Evolution 40, 699–711. https://doi.org/10.1016/j.tree.2025.04.010

Figuring out where species can live now, where they could live in the future as climate shifts or habitat changes, and where they may have lived in the past are key questions for which species distribution models (SDM) can be helpful. There are many approaches, but in general they use many location points for a species, gather info on climate or other factors at those points, and use them to model where a species can and cannot occur. For example, palm trees do not do well where it is cold enough for them to freeze: all their points will be from areas without multiple freezing days, and a good model will use this and other factors to predict where they can live (and perhaps moving poleward as climate tends to warm). But the problem is that many species lack the data required to do this well.

This paper sketches out a few ways to use information from a related species to help model a focal species. These can include using phylogenies to connect predictions or parameters between species as well as various other ways, such as co-occurrence, to have data-rich species help make inferenes for data-poor species.

I like this sort of work because it makes continuous how we often treat individual species. We say we avoid typological thinking (“any member of this group is fundamentally the same as any other member”) but we de facto use this when we assume all members of a species are identical. The reality is that there will generally be more or less closely related subpopulations that are not 1:1 replacements for each other (though likely exchanging genes), but it’s reasonable that the populations are pretty good predictors for each other. However, for traditional approaches to SDM, we assumes this predictive similarity stops at the (somewhat arbitrary) “species” boundary. With some of the methods in this paper, some info can flow between related species. It is a good use for phylogeny, especially in a world where the amount of data can vary so dramatically between species. Another example of this was done by Jess Welch and Jeremy Beaulieu for predicting bat extinction risk.

Another advantage of this paper for teaching this week is that it was a relatively short review paper (TREE). It can be a nice break for students more used to dealing with methods-heavy empirical papers.

@online{o'meara2025,
  author = {O’Meara, Brian},
  title = {PhyloPapers 2025, {Handling} {Data} {Deficiency}},
  date = {2025-10-24},
  url = {https://brianomeara.info/posts/phylopapers_2025_Oct_24/},
  langid = {en}
}

Verónica A. Rincón-Rubio, Rosana Zenil-Ferguson, Alejandro Gonzalez-Voyer. 2025. “The macroevolutionary consequences of the association between frugivory and carotenoid-dependent plumage coloration in passerine birds” Evolution, 2025, 79(8), 1643–1657 https://doi.org/10.1093/evolut/qpaf105

This is an example of a thorough study that investigates trait evolution well. The paper looks at potential correlation between eating fruits and having feathers that use carotenoids for color. It’s fundamentally a trait correlation question, but the paper is careful to first check for possible association between the traits and diversification. As Wayne Maddison showed nearly two decades ago, even if your question is about traits, ignoring the effect of differential diversification can cause problems: there are more state 1 than 0 at the tips because of a faster 0->1 than 1<-0 rate, and/or higher diversification rate in state 1, and/or there hasn’t been enough time to move on from a state 1 ancestral state: one can’t just look at the transition rates alone. So this paper does a ton of work to examine potential confounding effects, finds none, and then goes on to find that the ancestor for passerines likely had carotenoid-dependent plumage and that in species that do not have both carotenoid-dependent plumage and frugivory they lose the other trait quickly as well.

If I were to have a quibble with the paper, it would be its lack of units: for example, the rate of loss of carotenoid pigmentation in birds that don’t have frugivory (Fig 2) is “0.06” – no units indicated in figure or caption. Omitting units is standard for our field, but it can help with interpretation. For example, 0.06, assuming the tree branch lengths are in units of millions of years, is 0.06 events/MY or, perhaps more intuitively, an expected wait time per species of 1/0.06 = 16.7 MY to lose frugivory. Putting rates in terms of expected time until a change can help show if numbers are reasonable: this one, for example, suggests a leisurely amount of time for the change but still evolutionarily feasible: it’s not like an expected time of 10 years nor of 10 billion years.

Overall, though, this is a great paper. Another teachable moment from it – it could have come across as a boring null result: “carotenoids for plumage (or frugivory) do not affect speciation or extinction” and maybe even left in a drawer. But it’s not! For one thing, the lack of correlation is a discovery – I would have thought that carotenoid-dependent plumage would increase speciation (by allowing more reinforcement of mating barriers that appear). And there is an additional story about the trait correlation themselves, and potential ancestral states, that help us understand evolution better.

I made intro slides with some of my background material (SSE models, trait models) and some figures from the paper: PDF and PowerPoint.

To subscribe, go to https://brianomeara.info/blog.xml in an RSS reader.

@online{o'meara2025,
  author = {O’Meara, Brian},
  title = {PhyloPapers 2025, {Trait} Evolution},
  date = {2025-10-17},
  url = {https://brianomeara.info/posts/phylopapers_2025_Oct_17/},
  langid = {en}
}

Gil Yardeni, Michael H. J. Barfuss, Walter Till, Matthew R. Thornton, Clara Groot Crego, Christian Lexer, Thibault Leroy and Ovidiu Paun. 2025. “The Explosive Radiation of the Neotropical Tillandsia Subgenus Tillandsia (Bromeliaceae) Has Been Accompanied by Pervasive Hybridization” Systematic Biology https://doi.org/10.1093/sysbio/syaf039

This looks at subgenus Tillandsia inside genus Tillandsia, a group of morphologically quite diverse plants.

This paper was good for teaching because it was fairly focused: what is the phylogenetic network of the group and how is gene flow affecting this?

It’s yet another paper showing the utility of deleting massive amounts of data – in this case, looking at windows of the genome far enough apart that there is likely ample recombination between them. It was also useful in showing how one has to compromise. They used ASTRAL-III to infer a phylogenetic tree of all their samples incorporating processes like incomplete lineage sorting that might case gene tree - species tree mismatch. But they then used a much reduced number of taxa to analyze the history with zero to three possible reticulation events; given the massively larger search space when going from a tree to a network, they used a reduced set of samples. These were reasonable choices, but it shows the need for software that can feasibly look for reticulations for problems of hundreds of taxa or more.

The paper also used ABBA-BABA and related tests to scan along the genome to look for areas behaving unusually. Students have trouble understanding these at first (the ratio of the ‘wrong’ gene trees for sets of four lineages [ones that don’t match the species tree] having information about hybridization is a weird concept) but I think this paper explains it well, especially for an empirical paper not tasked with doing a review. It was also nice to see this being done as a genome scan as a discovery process. A lot of especially intro science is framed as rejection of dull hypotheses (“are these two different things not the same?”), but there’s still a lot of need for “let’s strap a camera on a sub and see what’s down there”, “let’s look at that galaxy in infrared”, “let’s see what Tillandsia are doing with their chromosomes”.

Side note I only briefly touched on in class: subgenus Tillandsia within Tillandsia?! “I study Tillandsia species,” even for someone using the same taxonomic concepts as oneself, is an ambiguous statement without including the rank. Yet another advantage of phylocode, where Tillandsia would have only one definition (and if one wanted to name smaller groups within it, one could without having to use subgenera, subsubgenera, superspecies, etc.). Unfortunately, Tillandsia as of today doesn’t seem to be defined at all in phylocode based on the online name repository; it might be in the Phylonyms companion volume to the code itself, but I lack a copy (but it’s on sale for $240 hardcover, only $225 for the eBook, so…).

I made intro slides with some of my background material and some figures from the paper: PDF and PowerPoint.

To subscribe, go to https://brianomeara.info/blog.xml in an RSS reader.

@online{o'meara2025,
  author = {O’Meara, Brian},
  title = {PhyloPapers 2025, {Reticulate} Evolution},
  date = {2025-10-10},
  url = {https://brianomeara.info/posts/phylopapers_2025_Oct_10/},
  langid = {en}
}

Sara E. Lipshutz, Mark S. Hibbins, Alexandra B. Bentz, Aaron M. Buechlein, Tara A. Empson, Elizabeth M. George, Mark E. Hauber, Douglas B. Rusch, Wendy M. Schelsky, Quinn K. Thomas, Samuel J. Torneo, Abbigail M. Turner, Sarah E. Wolf, Mary J. Woodruff, Matthew W. Hahn & Kimberly A. Rosvall. 2025. “Repeated behavioural evolution is associated with convergence of gene expression in cavity-nesting songbirds” Nature Ecology & Evolution. https://doi.org/10.1038/s41559-025-02675-x

[Note my conflict of interest: I was on Sara Lipshutz’ PhD committee]

One of the students in the class requested a modern paper on behavior. This paper had a lot of material for students to parse, but I think it’s good for them to see what a modern, chewy paper looks like. It has everything from careful natural history observations of birds attacking models to modern genomics to measures of hormones.

In all my classes I emphasize the difficulty of discretizing biology. Nature is full of variation. Even things that seem easiy countable, like number of limbs, have had variation over time and within species (quick, how many femurs do snakes have? Two in pythons). For this paper, a question was about aggression in cavity-nesting birds (birds nesting in holes in trees, for example) versus ones making open nests (the classic “use a crayon to draw a bird nest” nests). But even there, some birds only use cavity nests, some only use open nests, and some use both – how to categorize those? And does it matter if 99% of pairs in a species use a cavity or 3% of pairs use a cavity? Even continuous measures, like amount of aggression towards a model armed with a bluetooth speaker, require careful decisions. Is glaring at the model included in time spent in aggression? Flying around it yelling? Making full contact? This paper was useful in illustrating how these decisions are made.

The paper also had an interesting study design for a phylogenetics class. The authors chose non-overlapping pairs of species that differ in nesting habit. This is good, as it’s a lot like doing a twin study, but there still is differential relatedness between the pairs and so the authors used phylogenetic linear mixed models (PGLMMs – note, the “LLM” is not large language model) to control for relatedness. They also poked at the data in other ways to see if there could be confounding effects – for example, perhaps depth of the split between species in the pair had an effect on the results.

The genomic aspects I think were also informative. They looked for groups of genes that were expressed consistently differently between cavity and open nesters. There were a few found, but it felt appropriately preliminary: it wasn’t presented as “these are the genes that lead to aggression” but rather as candidates for potential further study. It was also an opportunity to teach students about how we understand putative gene function (from gene ontology databases).

I made intro slides with some of my background material and some figures from the paper: PDF and PowerPoint.

[Note that this blog post is dated for the class date, but I’m actually pushing it on Oct. 11, 2025]

To subscribe, go to https://brianomeara.info/blog.xml in an RSS reader.

@online{o'meara2025,
  author = {O’Meara, Brian},
  title = {PhyloPapers 2025, {Behavior} and Genomics},
  date = {2025-10-03},
  url = {https://brianomeara.info/posts/phylopapers_2025_Oct_03/},
  langid = {en}
}

Ornela N. Dehayem, Ryan F. A. Brewer, Luis Valente, Frederic Lens, Rampal S. Etienne. 2025. “Impact of sampling strategy on inference of community assembly processes in phylogenetic island biogeography”. Methods in Ecology and Evolution. 16:1507–1520. https://doi.org/10.1111/2041-210X.70058

Simulation is used a lot to validate methods, but I like teaching it to students because it’s also important in power analyses. In my experience it is still rare for students to figure out if their proposed research will have enough species, data, etc. to potentially answer their question before embarking on it. It can save so much future heartache to spend a few days seeing whether, for example, if this trait leads to evolution of this other trait at a particular rate there’s any chance of being confident in a result from a likely 17-taxon tree.

However, simulation is hard to do well. For one thing, the space of parameters to examine can grow very quickly. One of the reasons I first learned R was because I had done a bunch of simulations (perl to test a C++ program) to test some methods I made for species delimitation (O’Meara 2010) but I hadn’t realized how hard it would be to plot something like 6 variables at once (so why not bar charts on top of bar charts in an array of bar charts?). “Number of taxa might matter… and speciation rate… and the rate of trait evolution… and tree shape… and age… and…”. It can also be hard to figure out for a given parameter what values to try. Does this new method to estimate different substitution rates work well? On a three-taxon tree with mis-estimated branches, nope, it works horribly. On a 10,000-taxon tree where branch lengths are perfectly correlated with time, it works splendidly. But what will be relevant to biologists? That’s one of the reasons Jeremy Beaulieu and I (Beaulieu & O’Meara 2015) made sure to include units in a simulation so people could see whether they’re reasonable (even though one reviewer asked for us to delete units!). The ability to pre-determine the outcome of a simulation can also lead to odd choices depending on who is doing the simulating. For example, one paper in our field evaluated the performance of a Bayesian method written by many of the same authors by using simulation parameters that match the priors used in the later analysis. Unsurprisingly, it worked well when its prior was centered on the truth – with such a sim, it might work even better without any data! On the other hand, one could simulate using models that violate the assumptions of a method and then show that the method fails (“the normal distribution is a terrible way to get confidence interval for a mean… when data are simulated from a univariate uniform distribution”).

Dehayem et al. (2025) is interesting because it’s a paper testing a method created by many of the same authors, so presumably they’d be more accepting of results that show their method works, but it does a careful job. It is handling a complex scenario: arrival and diversification on islands, including hard to estimate parameters like extinction rate. There are some needed assumptions made in the simulation (for example, that this model describes the process so the parameter values are meaningful). It gets parameters from previously fit biological datasets, which is a good thing to do as it centers them on presumably realistic values. They also tried various ways to violate the model assumptions, such as a bias against sampling young species (there may be no gene flow, but humans haven’t recognized the populations as different species yet). There are many other potential things to vary, but it keeps it pretty focused and thus understandable. This made it an accessible jumping off point for discussions of simulations.

I made intro slides with some of my background material and some figures from the paper: PDF and PowerPoint.

[Note that this blog post is dated for the class date, but I’m actually pushing it on Oct. 11, 2025]

To subscribe, go to https://brianomeara.info/blog.xml in an RSS reader.

@online{o'meara2025,
  author = {O’Meara, Brian},
  title = {PhyloPapers 2025, {Simulation}},
  date = {2025-09-24},
  url = {https://brianomeara.info/posts/phylopapers_2025_Sep_24/},
  langid = {en}
}

Manica Balant¹, Daniel Vitales¹, Zhiqiang Wang¹, Zoltán Barina, Lin Fu, Tiangang Gao, Teresa Garnatje, Airy Gras, Muhammad Qasim Hayat, Marine Oganesian, Jaume Pellicer, Seyed A. Salami, Alexey P. Seregin, Nina Stepanyan-Gandilyan, Nusrat Sultana, Shagdar Tsooj, Magsar Urgamal, Joan Vallès, Robin van Velzen, Lisa Pokorny. 2025. “Integrating target capture with whole genome sequencing of recent and natural history collections to explain the phylogeography of wild-growing and cultivated Cannabis”. Plants People Planet. 1-18. https://doi.org/10.1002/ppp3.70043 [¹ = equal contributions]

This study examines wild and cultvated Cannabis, which is according to the paper has been used “as fibre (ropes, fabric and paper), medicinally (over 200 recorded uses), as food (nutrient-rich seeds) and in various magico-religious rituals”. It uses many samples from around the globe and genomic data to examine population structure.

One thing that has stood out in this paper and others considered for the course is the now apparent standard toolset of using mafft, Structure, ASTRAL-III, and IQ-TREE2. The age of some of the software is unexpected: the latest release of Structure is from over 13 years ago, and for ASTRAL-III it’s over five years ago. That’s not necessarily a bad thing – it’s not like hackers are trying to take over the banking system by exploiting potential vulnerabilities in unpatched phylogenetic software – it’s just surprising. You would think that especially for popular analytical questions there would be new innovations or just new hotness. For ASTRAL the developers “encourage using the new code” of ASTER, though as this only was published in a peer-reviewed journal in July 2025 it’s reasonable that papers we’re reading now don’t use it yet (and I doubt answers under ASTRAL-III are wrong). But I do worry as researchers continue to use ChatGPT and similar for analyses and ask “what’s the problem in doing stats with an (AI) consultant?” popular approaches will be baked into the training data and then will be continually suggested as the options to use, even when papers come out showing problems with classic approaches (and see all the studies STILL looking at net diversification rate through time) or new approaches unlock new questions. There’s already a bias towards that built into the field – it’s more efficient to use approaches one already knows (and you might know the limitations well, too, which is important), and all the tutorials or posts are about the classic software, but at least new info is constantly being put into our human brains, where we could have a preference towards recent approaches (though on R-sig-phylo there was a counterargument that the agentic AIs will learn about new approaches and suggest them). Though I guess one advantage of fossilization of methods advice is we’ll all start using MacClade again.

One thing I loved about the paper was the use of herbarium and fresh specimens. It’s yet another example of the benefits of repositories of biological information (recognized at many, but not all, places) as well as expertise to collect in the field. The paper also demonstrated the feasibility of genomic scale data.

A shift in thinking I’ve undergone but still feels unnatural is how in modern evolutionary biology a key workflow step is massively deleting data. It wasn’t that long ago we were looking at chromatograms in Sequencher to identify every possible base by eye (while still excluding bad reads); in contrast, in this paper the data were filtered from 68,212 single nucleotide polymorphisms (SNPS) down to just 2,875. This is important to do for data from GBIF (“nope, that oak is not from the ocean, someone flipped a sign”) and things like the TRY database of plant traits, too. This paper was useful for discussing this shift with students.

A conclusion from the paper was that Cannibis sativa is one species (as hypothesized by Linnaeus) and not two with the addition of C. indica. Perhaps a useful anecdote for intro bio when the standard move is to dunk on “Lamarckism” (though this requires more nuance than the cartoon version): the describer of the incorrectly split C. indica: Jean-Baptiste Lamarck. Though one student raised a good point: it would be interesting to redo the Structure analyses using only the wild plants, not escaped or domesticated cultivars – one possibility is that human meddling allowed interbreeding of C. sativa and C. indica that would have been relatively reproductively isolated otherwise.

One note for those who might want to teach the paper: I made sure to let students know that some uses of the plant are illegal in this jurisdiction (true at both our state and federal level) before asking them to provide information (for discussion questions on the paper before class, for example). I don’t want students trying to, say, make a joke, put in writing something that could be read by others as disclosure of illegal drug use – who knows how such info could be scraped into systems and misinterpreted in the future.

I made intro slides with some of my background material and some figures from the paper: PDF and PowerPoint.

[Note that this blog post is dated for the class date, but I’m actually pushing it on Oct. 11, 2025]

To subscribe, go to https://brianomeara.info/blog.xml in an RSS reader.

@online{o'meara2025,
  author = {O’Meara, Brian},
  title = {PhyloPapers 2025, {Phylogeography}},
  date = {2025-09-17},
  url = {https://brianomeara.info/posts/phylopapers_2025_Sep_17/},
  langid = {en}
}

Sonal Singhal, Adam D. Leaché, Matthew K. Fujita, Carlos Daniel Cadena, and Felipe Zapata. 2025. “A Genomic Perspective on Species Delimitation” Annu. Rev. Ecol. Evol. Syst. 2025. 56:467–89 https://doi.org/10.1146/annurev-ecolsys-102723-055311

In contrast to the other papers so far in the class, this is a review paper rather than original research (though, like many modern review papers, it actually does a fair amount of new bibliometric analysis). I’m not going to have us read a lot of review papers, but this one touched on various topics that came up in previous discussions (like the multispecies coalescent) and was a bit of a breather for students in that it had less jargon. They loved it and found it understandable. I think it was a very thorough and fair overview of the subject.

The two biggest questions students had were about the machine learning mentions and the gdi metric (Jackson et al. 2017). People now often think of AI as large language models like ChatGPT, but it covers a wide variety of approaches: image recognition, like that used by iNaturalist; approaches to impute missing data; methods to infer relationships from large sets of data; etc. One could imagine hoping for a genomic equivalent for the long wished-for “barcoding gap” (a discrete difference between intra- and interspecific differences), for example. The gdi is a measure we came up with to compare differences between potential species: it is scaled from 0 for panmixia to 1 for strong divergence. (I actually fought my coauthors, because I didn’t really like the gdi, but my collaborators won out and they were right). I think part of the confusion from the Singhal et al. paper is that the gdi in their Fig 2 is shown as a property of a population, when it’s actually a measure used between two populations.

One thing that both I and the attendees liked was the discussion at the end of the paper about not making genomic approaches a requirement for species delimitation publications. Genomics brings a lot of possibilities (such as looking for genomic regions correlated with lack of gene flow), and it may even be cheaper than older sorts of data which have less information. But genomic data are still not feasible for all researchers or locations. Especially given the need to do basic species discovery in so many groups, using available data to understand biodiversity is more critical than waiting until one can do multiple full genomes.

An interesting data point from the bibliometric analysis was that only in 36% of cases did discoveries about a need to change taxonomy lead to a taxonomic change in the papers. This is in line with my anecdotal experience. There are good reasons for this (a taxonomic change is a big deal and requires expertise – one doesn’t want to rush into it) but it is a bit odd that methods intended to discover new species or collapse oversplit ones usually don’t lead to this in a usable way.

Overall, I expect this paper to become widely used in teaching and for people getting into the field.

I made intro slides with some of my background material and some figures from the paper: PDF and PowerPoint.

To subscribe, go to https://brianomeara.info/blog.xml in an RSS reader.

@online{o'meara2025,
  author = {O’Meara, Brian},
  title = {PhyloPapers 2025, {Genomic} Species Delimitation},
  date = {2025-09-12},
  url = {https://brianomeara.info/posts/phylopapers_2025_Sep_12/},
  langid = {en}
}

Molly Chen, Artem I. Kholodov and Laura A. Hug (2025). The evolution of the tree of life. Phil. Trans. R. Soc. B 380: 20240091. https://doi.org/10.1098/rstb.2024.0091

This is a really cool paper: it took snapshots of the info in GenBank at five year intervals, built trees from the available info at each time step, and compared how the trees changed over time. It also looked at trees from key papers over time. I also liked it for class as it touched on long branch attraction and tree distances, which are useful background for students to know. The paper also generated discussion of tree construction, including use of backbone trees, as well as the need for taxonomic name resolution and massive deletion of sequences (for example, going from 2.2 million genomes down to 333K after filtering). I still remember the days of fighting hard for every base pair, so the shift in the genomic era to tossing most info is still viscerally painful, but useful.

This is also a paper where the supplement has some genuinely useful info: it’s available at https://doi.org/10.6084/m9.figshare.c.7897347.v1.

One thing that became apparent is how many of us, myself included, have nearly all our natural history and taxonomic knowledge focused on eukaryotes. Even issues like the possible lack of monophyly of Archaea were revelations to many: the three domain view of life is often still taught as the default. I plan to read more widely to understand the diversity of life better (my secret hope is that at some point Riley Black will write a book on single-celled life as engaging as her other ones…).

I made intro slides with some of my background material and some figures from the paper: PDF and PowerPoint.

To subscribe, go to https://brianomeara.info/blog.xml in an RSS reader.

@online{o'meara2025,
  author = {O’Meara, Brian},
  title = {PhyloPapers 2025, {Growth} of the Tree of Life},
  date = {2025-09-06},
  url = {https://brianomeara.info/posts/phylopapers_2025_Sep_05/},
  langid = {en}
}

Update (Sept 9, 2025):

GRFP is LIVE!!! https://www.nsf.gov/funding/opportunities/grfp-nsf-graduate-research-fellowship-program

Deadline is Oct. 27, 2025, for life sciences (which suggests they plan to fund life sciences), and shortly after that for other fields. There still isn’t a full solicitation (the detailed guidelines of what they want in a proposal), and they oddly do not link to the old solicitation. This could mean they’re changing something (page count, what they ask, etc.) but it’s hard to know. This is also fewer days than I had been expecting given the policy of 90 days from them announcing an opportunity to applying, but maybe they’re considering that the fact that this is a renewal of a program as meaning they don’t have to meet the window (and really, who would rather them have a deadline go much later so grad students and programs don’t know about GRFP funding until very late in the cycle?).

Another interesting part of the webpage is that the estimated number of awards is:

1 to 3000 - NSF will support up to 3000 new Graduate Research Fellowships per fiscal year under this program solicitation pending availability of funds.

That is different from last year’s language of

Estimated Number of Awards: 2,300

NSF will support up to 2,300 new Graduate Research Fellowships per fiscal year under this program solicitation pending availability of funds.

They’re giving a 1-3,000 range rather than what typically is the expected number of awards. My guess is they’re still hoping to get a large number of awards. Given the potential external funders, maybe NSF is leaving room for them to fund the typical awardees plus honorable mention students. Though it also opens the possibility that in March NSF will announce “congrats to the NSF GRFP awardees: Martha. Just Martha.”

Regarding availability of funding, there is currently uncertainty about a potential government shutdown at the end of September (as well as uncertainty about next year’s NSF funding in general). During a shutdown, NSF staff will not be able to respond to any questions; with some shutdowns, government websites go dark (in the past I’ve archived ones), and deadlines might be pushed back (but do not count on it). Potential applicants should thus stay on top of this news and prepare.

@online{o'meara2025,
  author = {O’Meara, Brian},
  title = {GRFP 2025},
  date = {2025-09-04},
  url = {https://brianomeara.info/posts/grfp_2025/},
  langid = {en}
}

Laura P. A. Mulvey, Mark C. Nikolic, Bethany J. Allen, Tracy A. Heath and Rachel C. M. Warnock (2025). From fossils to phylogenies: exploring the integration of paleontological data into Bayesian phylogenetic inference. Paleobiology 51, 214–236. https://doi.org/10.1017/pab.2024.47

It has good background on Bayesian approaches. It also brings in fossil data, which may appeal to some of the paleontologists in the class. The paper is very much in favor of Fossilized Birth Death (FBD) models, and is honest about this: “Through this review, we hope to celebrate the FBD model family, inform readers of its nuances and potential uses, and stimulate discussion around best practices for answering empirical questions.”

I agree these models are useful, but there can be caveats. Jeremy Beaulieu and I incorporated FBD models with likelihood in hisse, and tested the effect of less than perfect sampling of fossils:

Jeremy M Beaulieu and Brian C O’Meara (2022) Fossils do not substantially improve, and may even harm, estimates of diversification rate heterogeneity. Systematic Biology 72(1), 50-61. https://doi.org/10.1093/sysbio/syac049

I’m not having the students read our paper (we’re still doing intro about tree building; I think doing both would be too much nuance at this stage), but others curious about FBD models might be interested and may overlook it (Mulvey et al. (2025) don’t cite or mention it in their review, for example). The implementation is too slow for tree inference, but it can be used for estimating various diversification and fossil rates under a likelihood approach, so not requiring (or benefitting) from priors.

For teaching, I really like how the Mulvey et al. (2025) paper gets into the various uses of the FBD model: to help with inferring the tree, dating, estimating parameters, etc. A lot of students think of models as tests, fit for one purpose, but models are general ways of representing the world and can be put to many uses. For example, Brownian motion models of continuous traits can be used for estimating ancestral states, but they can also help infer trees or for estimating rates of evolution, too: they’re just a representation of how traits change over time under various assumptions. The same for FBD.

I made intro slides with some of my background material and some figures from the paper: PDF and PowerPoint

One thing I’m going to do this week is talk less – last time the mini-lecture went on too long, but I want more student interaction.

To subscribe, go to https://brianomeara.info/blog.xml in an RSS reader.

@online{o'meara2025,
  author = {O’Meara, Brian},
  title = {PhyloPapers 2025, {Bayesian} Analysis},
  date = {2025-08-29},
  url = {https://brianomeara.info/posts/phylopapers_2025_Aug_29/},
  langid = {en}
}

1. What was the discovery or innovation in this paper?
2. How this was supported?
3. What didn’t you understand about the paper, the methods, the questions, and/or the organisms?

My goal is to make it a positive learning experience – sometimes classes focusing on papers can devolve into “paper shredding” sessions where everyone takes pot shots at it. This can be a cheap way to perform competence at one another, but it’s not fair to the paper’s authors and also can lead to cynicism about the possibility of learning. Nearly all papers have a mixture of good and bad features, often due to very real constraints we all face, but they also do contribute insights about the world.

For the first week I wanted to show student a recent paper where making the phylogeny helped with understanding the biology. The focus is to have students learn a bit about why and how we make phylogenies and then build in deeper. I chose

Alison R. Irwin, Nicholas W. Roberts, Ellen E. Strong, Yasunori Kano, Daniel I. Speiser, Elizabeth M. Harper, and Suzanne T. Williams. 2025 “Evolution of Large Eyes in Stromboidea (Gastropoda): Impact of Photic Environment and Life History Traits” Systematic Biology 74(2):301–322. https://doi.org/10.1093/sysbio/syae063

as it uses a few different phylogenetic approaches in an appealing study system.

I made intro slides with some of my background material and some figures from the paper: PDF and PowerPoint

Some of the material used:

• The paper: https://doi.org/10.1093/sysbio/syae063
• Sequences: https://doi.org/doi:10.5061/dryad.pnvx0k6v0
• Other background info: https://doi.org/10.5281/zenodo.13768189 (though I had to use version 1 as the more recent one is under embargo)
• Mesquite for visualizing the sequences

Note I wasn’t involved in the Irwin et al. (2025) paper at all (not as a reviewer, AE, etc.), so I don’t have any insights into its development.

To subscribe, go to https://brianomeara.info/blog.xml in an RSS reader.

@online{o'meara2025,
  author = {O’Meara, Brian},
  title = {PhyloPapers 2025, First Class},
  date = {2025-08-22},
  url = {https://brianomeara.info/posts/phylopapers_2025_Aug_22/},
  langid = {en}
}

• A file of functions.
• A list of targets that use your functions and other functions to create a series of steps

It’s much more logical than the mixture of functions and workflow you see in a lot of beginning R user scripts. It also saves time: if you have a three step process, A -> B -> C, you can run it through once, decide step B needs to be tweaked, change the function for that step, and it will know to run only steps B and C again, keeping A unchanged. This is great for doing simulation analyses: “oh, let’s add more conditions to understand the behavior between t=5 and t=50, as that is where things seem to change” – only the new values will be run. I use targets for everything from analyses and creating figures for papers to creating a multi-thousand page website for helping my kids compare colleges (as one does).

It has great abilities for working on clusters and other high performance computing structures. We ran into an issue, though, with running on a colleague’s beefy multicore computer (which isn’t running queueing software, but it’s one with a large amount of RAM and over a hundred cores). We used crew with targets to handle batching out jobs to the cores. The issue is that while this could make sure there were cores available, it was not tracking memory use. The jobs we were sending out involved doing analyses on simulated trees: these could be just a handful of species or many thousands, and so the jobs were wildly different in terms of memory usage. If we figured out the average memory usage for a job and set to not hit the cap on average, there was still a chance of using up too much memory, especially as smaller jobs finished earlier so the population of running jobs becomes more enriched for slow, high memory jobs later in the run. On the other hand, if we conservatively set the number of cores used to a low number, we’re taking longer to run things than we need to as we’re nowhere near the memory or core limit. Perhaps with a more sophisticated batching system memory use would be incorporated, but it’s an odd kind of job for this because memory need varies a lot based on the input object.

So, a kludge I made:

To do parallel, we use crew:

tar_option_set(
        packages = c( "tidyverse",  "tarchetypes", "ggplot2", "RColorBrewer", "ggrepel", "ggtext",  "pdftools", "readr", "parallel", "phytools", "TreeSim", "viridis", "RPANDA", "epm", "parallel", "ggbeeswarm", "JuliaCall", "memuse"),
        controller= crew_controller_local(workers = 80, seconds_timeout = 600, garbage_collection = TRUE)
)

Our relevant slow target is:

    tar_target(
        inference_results,
        command = DoInference(many_trees),
        pattern = map(many_trees),
        iteration = "list"
    )

Where we have simulated trees from previous simulation steps in the list many_trees.

In the DoInference() function, I set a threshold of 10 GB for desired amount of free RAM on the cluster (free_GBram_threshold) then used this code before the actual analysis starts:

    Sys.sleep(runif(1, 0, ntax)) # sleep for a random amount of time to avoid all processes starting at the same time, have bigger trees start later
    freeGBram <- .Call(memuse:::R_meminfo_raminfo)$freeram / 1e9
    while(freeGBram < max(free_GBram_threshold, sqrt(ntax))) {
        Sys.sleep(10)
        freeGBram <- .Call(memuse:::R_meminfo_raminfo)$freeram / 1e9
    }

ntax is the number of species on a tree, roughly proportional to how big a job it will be to run.

This uses the memuse package to see how much RAM is available on the system, and then does a loop to wait until there is enough free before starting the expensive part of the analysis. If there is enough RAM, everything runs happily, with a slight bias for starting easier jobs sooner. As RAM fills up, the same number of jobs are technically running, but some are just doing repeated sleep cycles until there’s memory capacity. This isn’t ideal – there could be low-memory jobs that would fit easily, but the big jobs are just occupying a core running Sys.sleep() forever – but for our setup, where there was one user on the computer trying to juggle memory, it worked ok.

To subscribe, go to https://brianomeara.info/blog.xml in an RSS reader.

@online{o'meara2025,
  author = {O’Meara, Brian},
  title = {Watching Memory in Targets in {R}},
  date = {2025-07-25},
  url = {https://brianomeara.info/posts/targetsmemory/},
  langid = {en}
}

Additionally, there are several specific Special Interest Group (=: SIG) mailing lists; however do post to only one list at time (‘SIG’ or general one), cross-posting is considered to be impolite.

The phylogenetics one has been a good place for discussion and learning; I even quote posts from it at length in the help for ancestral state reconstruction using Ornstein-Uhlenbeck models in the OUwie documentation, as Joe Felsenstein, Dave Bapst, Simone Blomberg, Marguerite Butler, Nick Matzke, and Thomas Hansen all weighed in (I wrote the function so that by default it gives an error until a user indicates they actually read the documentation about the risks of this analysis).

However, lately the list is filled with posts for courses in R. It might not be unreasonable: a workshop in R for phylogenetics, like the evolutionary quantitative genetics workshop led by community members Josef Uyeda and Fabio Machado, could be appropriately sent to the list. But now the list nearly all ads for courses, most of which do not relate to phylogenetics. Out of the last 50 threads on the list (Feb 2025-July 2025):

• 22 were by Carlo Pecoraro of Physalia Courses
• 9 were by Oliver Hooker of PR Statistics
• 8 were by Soledad De Esteban-Trivigno of Transmitting Science

Nearly all of these were for paid courses offered by these groups.

There were also two (2!) threads with actual questions on R in phylogenetics (only one of which had answers), a request to nominate scientists for a medal, and two posts from the long-serving volunteer list moderator, Hilmar Lapp, about proper conduct, including one about too many training course advertisements. Hilmar’s request was that groups post no more than two courses a month and only post each course once (no “last chance of” or similar follow-ups). All the companies above violated this (links to examples): Physalia (which also posts over 3.5 courses per month), PR Statistics, and Transmitting Science.

An easy criticism would be to argue for different moderation choices or actions, but any such move puts even more work on a long-serving community member. Why should someone volunteer their time to play whack-a-mole with multiple people who are paid to send out announcements as part of their day job? It’s not sustainable. It would be better if companies decided to be responsible and respect listserv rules (including the R-project’s request not to cross-post). They could even start their own R-sig listserv of courses that people could opt into – there’s a need for quality R courses, after all.

Use of mailing lists just to sell courses is even worse outside R-sig-phylo – for example, in June the 28 threads in the R-sig-ecology list were ALL for courses (11 from Physalia, 8 from PR Statistics, 6 from the Institute for Statistical and Data Science, 2 from Transmitting Science, 1 from Highland Statistics), despite the huge number of R users in ecology, and the trend is continuing in July.

Email discussion lists are definitely declining (you can see looking through the R special interest lists how many are practically abandoned) – we all get too many emails, and in some ways social media has replaced the need for mailing lists to ask questions and get answers (see #rstats on bluesky, for example). But one could imagine that in the coming era of AI slop, mailing lists with responses limited to those written by actual humans would once more become essential. It’s sad to see something like R-sig-phylo, which has been a place for good discussion and could be again, get overwhelmed by companies choosing to use it to sell courses: who wants to join or stay on a list if only 4% of posts are directly connected to the list topic? I myself was considering unsubscribing this morning – thus this plea for things to change (and I might as an intermediate step just block any mail from particular companies).

@online{o'meara2025,
  author = {O’Meara, Brian},
  title = {Course Ads on {R-sig-phylo}},
  date = {2025-07-18},
  url = {https://brianomeara.info/posts/rsig/},
  langid = {en}
}

This is intended as a guide for the Evolution meetings, which happen most commonly in the US (where the majority of the members are) but which do rotate around the world (Brazil, New Zealand, France, Canada, and more). For years the meeting locations were decided based on avoiding states on the California travel ban for both potential safety reasons and due to limitations on travel from scientists in California, but California has now repealed this ban; other factors considered include cost (of travel, lodging, and the convention center), other safety issues, and spreading out where the meetings are. Appeal of the location also matters – the meetings are run to break even, not make or lose money, but having a location where people want to go is important.

The committee noted how much members valued different factors and used these to develop a weighted rubric for choosing locations within the US. There is a separate level of decision about which country to have a conference in, and that can change radically with geopolitical realities, visa restrictions, distribution of members, and much more – this is an increasingly important question but was out of scope for the committee.

So, assuming the conference will be somewhere in the US, the rubric on locations is:

Lower scores are better. The sources they pointed to for data included:

• https://everytownresearch.org/rankings/
• https://www.justice.gov/hatecrimes/state-data
• https://www.lgbtmap.org/
• https://bjs.ojp.gov/sites/g/files/xyckuh236/files/media/document/naacp_hate_crime_laws_by_state.pdf
• https://www.ncsl.org/civil-and-criminal-justice/racial-and-ethnic-disparities-in-the-criminal-justice-system#anchor5408
• https://states.guttmacher.org/policies/
• https://www.lgbtmap.org/equality-maps/healthcare_laws_and_policies
• https://www.lgbtmap.org/equality-maps/nondiscrimination/bathroom_bans

This is not the only factor. For example, cost for travel and lodging can vary substanially between locations, and does not always follow conceptions of “expensive” places – a place on the US West Coast with lots of direct flights might be cheaper than a place with a lower cost of living but where most attendees would have to take two or three connecting flights.

One issue that has come up in the past with the California ban list is changing conditions between when a conference is scheduled and when it is held: for example, Ohio was considered fine for years, then it changed its laws. For a meeting like the Evolution meetings, with around 1500 attendees, conference centers are reserved 4-5 years in advance. Things like laws on healthcare access can change dramatically over that time; they can also change at a national level making any state laws moot. Moving can result in large financial penalties – one back of the envelope calculation suggested changing one particular venue might result in hundreds of dollars more per individual registration. It can thus be infeasible to move events even with a year or two warning, making it even more important to try to understand trends before picking locations.

For more detail on the committee’s reasoning and their insights from their survey, check out their PDF. You can see more about choosing locations and other questions for the Evolution meeting here.

To subscribe, go to https://brianomeara.info/blog.xml in an RSS reader.

@online{o'meara2025,
  author = {O’Meara, Brian},
  title = {Conference Locations},
  date = {2025-06-19},
  url = {https://brianomeara.info/posts/conferencelocations/},
  langid = {en}
}