I hope to take a PhD student in 2026 to study siphonophores. Please reach out if you are interested in applying this fall. For examples of recent lab projects see www.sciencedirect.com/science/arti... and academic.oup.com/gbe/article/...
Once upon a time, large bone-crushing dogs roamed across our continent. @restingdinoface.bsky.social takes a colorful look back at the lives of the successful carnivores. www.smithsonianmag.com/science-natu...
Going from reading almost exclusively simulation studies and theory papers to almost exclusively canid systematics/fossil description papers has been an interesting experience!
Anyway look at this cool paper about Plio-Pleistocene European foxes!
www.sciencedirect.com/science/arti...
They're listening to my private conversations somehow, I'm certain of it.
Holy crap I'm excited 🤩
Updated preprint! Thanks to suggestions from reviewers, we have some new insights on using continuous traits in phylogenetic estimation.
And a lot more about dicynodont taxonomy. Seriously, google dicynodonts and tell me you don't want to know what's happening there
www.biorxiv.org/content/10.1...
The brilliant tapestry of young stars flaring to life resemble a glittering fireworks display in this Hubble Space Telescope archival image. The sparkling centerpiece of this fireworks show is a giant cluster of thousands of stars. The nebula reveals a landscape of pillars, ridges, and valleys. The pillars, composed of dense gas and thought to be incubators for new stars, are a few light-years tall and point to the central star cluster. Other dense regions surround the pillars, including reddish-brown filaments of gas and dust. The red dots scattered throughout the landscape are a rich population of newly forming stars still wrapped in their gas-and-dust cocoons. The brilliant blue stars seen throughout the image are mostly foreground stars. The red colors in the nebulosity represent hydrogen; the bluish-green hues are predominantly oxygen. Caption credit: NASA
A globular cluster that looks like a very dense, ball-shaped collection of many shining stars in colors of white, yellow-orange, and blue. Some stars appear a bit larger and brighter than others, with the brightest having faint cross-shaped diffraction spikes. The cluster’s stars are scattered mostly uniformly, with their density increasing toward the cluster’s core where they merge into a strong, bright-white glow.
This image of Cassiopeia A resembles a disk of electric light with red clouds, glowing white streaks, red and orange flames, and an area near the center of the remnant resembling a somewhat circular region of green lightning. X-rays from Chandra are blue and reveal hot gas, mostly from supernova debris from the destroyed star, and include elements like silicon and iron. X-rays are also present as thin arcs in the outer regions of the remnant. Infrared data from Webb is red, green, and blue. Webb highlights infrared emission from dust that is warmed up because it is embedded in the hot gas seen by Chandra, and from much cooler supernova debris. Hubble data shows a multitude of stars that permeate the field of view.
A prominent, eight-pointed star shines in bright white at the center of this image. A clumpy cloud of material surrounds this central star, with more material above and below than on the sides, in some places allowing background stars to peek through. The cloud material is a dark yellow closer to the star, and turns a pinkish purple at its outer edges. Combined together, the central star and its cloud resemble the delicate petals of a cherry blossom. The black background features many smaller white stars scattered throughout.
here are some space fireworks, happy 4th of july 🎆🌌
We’re hiring! Join us at maraujo lab as a #PhD candidate to model species abundances using cutting-edge ecological and statistical tools. Based at @mncn-csic.bsky.social @mncn-bgcg.bsky.social and @utrechtuniversity.bsky.social.
🔗 www.maraujolab.eu/2025/07/04/p...
#SpeciesDistributionModels
Having to write an R script to write another script makes me feel like I am in limbo
When a chatbot gets something wrong, it’s not because it made an error. It’s because on that roll of the dice, it happened to string together a group of words that, when read by a human, represents something false. But it was working entirely as designed. It was supposed to make a sentence & it did.
Chatbots — LLMs — do not know facts and are not designed to be able to accurately answer factual questions. They are designed to find and mimic patterns of words, probabilistically. When they’re “right” it’s because correct things are often written down, so those patterns are frequent. That’s all.
It’s great to see researchers exploring the possibilities of learning about current extinction from past extinction patterns. That was my initial idea for my PhD, and I have deviated from that pretty hard since then, but I still hope to get on that wagon later in my career!
Very cool paper! Glad to see Bluesky helping me find new cool science hot off the press 🍞
🆕 Preprint now on bioRxiv!
We introduce a covarion model for phylogenetic inference using discrete morphological data, addressing lineage- and character-specific rate heterogeneity.
www.biorxiv.org/content/10.1...
#phylogenetics #morphology #covarion #evolution
#MolluscMonday Ordovician orthoconic nautiloid in a paving stone at Hampton Court Palace. The section intersects the siphuncular tube on the right hand-side just below the living chamber.
Excellent analysis & commentary by @johnhawks.net on 2024 study of 1.5 mya fossil hominin footprints in Kenya showing 2 different species of humans (Homo erectus & Paranthropus boisei) walking in the same direction, & possibly separated by only a few hours. Ichnology FTW! 🧪🐾🪨
For those who followed me without knowing me (thank you and nice to meet you!), it’s very important for you to know that I am unhealthily obsessed with horseshoe crabs.
I don’t even study them at all, and they’ve just been my favorite animal since I found out they existed. They’re the coolest!
Thank you! Would love to talk more about it any time!!
13/13 And that's that! Here's the full talk. I'll record a longer, higher quality version later, and post it here with a more detailed rundown. And of course I'll let you all know when the paper is published! Lots I still want to do for this project.
Stay tuned!
youtu.be/aS31GnXh3ec
12/13 I'd love to sit here and say "yes, this easier model provides reasonable trees, keep using it!", but SRFBD is the only way to truly accommodate for this data. I think it's worth the implementation challenge, and that it is the future of fossil phylogenetics. And look at this beautiful tree!
11/13 We need simulations to be sure of which model is more accurate, but the fact that we can identify specific model violations and connect them to biases in our estimates is concerning. We tried a different workaround than usual for FBD specimen, and unfortunately it doesn't seem to have helped!
10/13 This, together with the fact that we are not using all the samples for each of our species, might be related to why we estimate lower fossil sampling rates and higher speciation rates with FBD specimen. This leads to younger estimates of divergence times.
9/13 While the first specimen of Epicyon haydeni is correctly placed as a sampled ancestor of the last, the opposite is true for Epicyon saevus. They're tips, and at the same time! Compare that to the same node in our SRFBD tree and you can see the issue.
8/13 So the model is telling us Archaeocyon is a canine! Very interesting, and something to look into.
We do need to talk about where the FBDS workaround we did (using both the youngest and oldest specimens for a given species) fails. In the MCC tree using both specimens, we see some of this:
7/13 A cool example of where they agree: both models estimate a really low posterior probability (<0.005) of the monophyly of Borophaginae. The monophyly of Borophaginae minus Archaeocyon (the first-diverging genus), however, is pretty likely. So is the monophyly of (Archaeocyon, Caninae).
6/13 There's no good way to summarize budding trees currently, so to compare the trees I made MCC trees from a modified posterior sample, removing the first specimen of each tree. So this is a tree the way you're used to: one tip per species. You can see they're pretty similar (RF-distance: 0.19)
5/13 A limited implementation of SRFBD (allowing for budding speciation only) is implemented in BEAST2 as part of the sRanges package.
Check out the package: shorturl.at/nOncC
And the preprint: shorturl.at/fzObi
I used BEAST2 to do two combined-evidence analyses to compare FBD specimen and SRFBD.
4/13 The stratigraphic-range FBD (SRFBD) process corrects these issues by explicitly accounting for taxonomy. Here, the data input are stratigraphic ranges, not individual fossil specimens. Then we can build a phylogenetic tree using all our data, and it even allows for budding speciation!
3/13 FBD assumes that each specimen is an independent sampling event, which is violated if you use multiple specimens per species. But if we use just one for a species that has multiple specimens (e.g. most canids), we bias divergence times since we ignore the uncertainty on each specimen
2/13 The problem: estimating a complete (i.e. including fossil species) phylogenetic tree of Canidae is challenging with the FBD process, because FBD is a taxonomy-independent model. Like many paleontological datasets, the canid fossil record is structured through a filter of taxonomy.
1/13 Time to talk about dogs and fossilized birth-death models! Here is a quick run-down of my #Evol2025 talk. 🧵