Diversification rate shifts are everywhere. Analyses of phylogenies covering >300k species reveal widespread changes in speciation dynamics across the Tree of Life.
@bjorntko.bsky.social @hoehna.bsky.social @acapomorphic.bsky.social academic.oup.com/evlett/advan...
Photograph of a fossil fish skull in right lateral view. The bone is dark brown/black against a gray matrix.
Out now in Contributions from me and @gilespalaeo.bsky.social, a deep dive into an early member of the sturgeon and paddlefish lineage. Bear with me, but there’s a long backstory highlighting uncertainty about the anatomy of living species and how well-studied fossils can still yield new insights.
Cover of the journal Nature, featuring the head of a large fish with its mouth open. A smaller fish is swimming into its mouth. The cover reads "Caught in Time: Early fossils shed light on the origins of bony fish."
Osteichthyans--the bony fishes--are by far the most diverse group of living jawed vertebrates. Two papers out today in @nature.com feature remarkable new Chinese fossils that paint a picture of substantial morphological diversity among stem osteichthyans.
Our new paper revealing major patterns of diversification across the Tree of Life is here!
doi.org/10.1093/evle...
Congratulations to @bjorntko.bsky.social for leading this ambitious effort, it was a pleasure to contribute alongside @acapomorphic.bsky.social, @hoehna.bsky.social & Luis Palazzesi.
We hope that our study will provide the foundations for understanding the nature of diversification rate shifts in exceptionally species-rich clades. You can read it open access in Evolution Letters: doi.org/10.1093/evle...
@bjorntko.bsky.social @jclarkepaleo.bsky.social @hoehna.bsky.social
- Shifts in diversification rate happen more frequently in younger phylogenies
- Groups with faster diversification overall also show higher variability in diversification rates
- Flowering plants appear to experience larger (and more frequent) shifts than any other group we analyzed
A few key findings:
- Shifts in diversification rate are ubiquitous across the Tree of Life: the process of diversification is highly heterogeneous
- Upshifts are way more common than downshifts (but this is likely due to lack of power in detecting downshifts with extant phylogenies)
New paper out from @hoehna.bsky.social Lab, led by the brilliant @bjorntko.bsky.social! We applied the Pesto software (Kopperud & Höhna, 2025) to look at lineage-specific shifts in diversification rate on large, densely-sampled phylogenies across the Tree of Life doi.org/10.1093/evle...
Our new paper 'A covarion model for phylogenetic estimation using discrete morphological datasets,' is out in SysBio!
We introduce the "covariomorph" model in RevBayes to capture character and lineage specific rates of morphological traits.
🔗 doi.org/10.1093/sysb...
A group of smiling paleontologists standing in front of signs reading "REGISTRATION" and "SVP 2025." Photo credit: Sam Giles.
Michigan paleontology family portrait, Birmingham edition #2025SVP
I'll be presenting a poster at #2025SVP #SVP2025 about my recently published work on how many characters are needed to reconstruct a phylogeny. Come by at the poster session this Thursday if you want to chat about it! royalsocietypublishing.org/doi/abs/10.1...
Ah, I see! That is definitely true, but that same character discretized vs in its continuous form carries a very different quantity of information. If modeled properly (maybe a big if), one continuous character should have more information content to estimate a phylogeny than its discretized version
I like the optimism there! I guess what would be good to know is: given the amount of "perturbation" from the true phylogeny that I can expect based on the size of my data, are the main patterns I'm interested in (diversification, biogeography, phenotypic evolution) robust to that much perturbation?
Continuous characters are not that commonly used in Bayesian morphological phylogenetics though. I want to believe that those can be a mostly unexplored source of information to infer evolutionary relationships, although I'm very aware that they come with their own set of issues and limitations
In my simulation, a phylogeny inferred for 50 taxa with 50 binary characters on average has 50% of the nodes wrong (and this is with no model misspecification and no missing data). What can we do about it? I don't have any clear/easy solution, but at the same time I don't want to be too pessimistic
I totally agree, for some systems there is possibly an intrinsic limit on the number of (more or less) independent variable characters that can be defined and scored that is lower than 100. Then the question is: what can we do for those? Do we just accept that our phylo estimates will always be off?
Thank you! I would expect that adding rate heterogeneity to the model (which means adding parameters) would require at least the same minimum number of characters, if not more.
Problem: a lot of empirical morphological datasets have fewer than 100 characters, and way fewer than 500. Possible solutions? Continuous characters; total-evidence datasets; more funding, hiring, training targeted towards characterization and digitization of interspecific morphological diversity.
An important point: the 100-500 chars threshold refers to an ideal scenario where we know under which model the data evolved (no model misspecification), this model is relatively simple (few parameters to infer), and there is no missing data. Thus, this should be taken as a very minimum number.
One intriguing empirical application of these findings is that, for more than 50 taxa, characters that change multiple times independently across the tree (homoplastic characters) improve tree reconstruction compared to characters that change only once (synapomorphies and autapomorphies).
Overall, between 100 and 500 variable characters are necessary to reach sufficient accuracy and precision of phylogenetic estimates for as low as 20 taxa. This is relevant not only for morphological phylogenetics, but also for gene trees and SNP-based estimates, and for Bayesian phylolinguistics.
Three different metrics of accuracy and precision were used to evaluate how good was the phylogenetic inference. General resulting patterns: more characters are better; more states are better (but this has little effect for >50 taxa); more taxa are worse for short trees, but better for long trees.
I designed this simulation study in RevBayes to perfectly match the models used for sim and inference. Any differences between the true tree generating data and the inferred tree(s) are due to dataset size. Parameters that varied across sims are: # characters, # taxa, tree length, and # states.
Out now in Biology Letters, my latest paper tackles an apparently simple question: how many characters are needed to reconstruct a phylogeny? TL;DR: in most cases between 100 and 500, more than a substantial portion of morphological datasets, but the story is more complex... doi.org/10.1098/rsbl...
Just in time for #FossilFriday 🦖 What are the big questions in #paleontology today?
dx.doi.org/10.1017/pab.2025.10042
Nearly 200 scientists worldwide came together to map where our field is headed. Here’s the story 👇
What are the biggest questions in #paleontology? New paper out today in Paleobiology led by Smith & Kiessling with ~200 coauthors on the relevance of our field, methods, & museum collections to climate & biodiversity research🦖 #FossilFriday @paleosoc.bsky.social 🔗: www.cambridge.org/core/journal...
I'm afraid we forgot to mention any lemurs there... 😅 But I hope you're still using them for Analytical Paleo! 😁
Thanks Jeff! The VP lecture slides definitely left an impression, I had to use the bird somewhere 😅
Ever wondered how to incorporate fossils as tips in a phylogenetic tree?
Our new paper provides a comprehensive guide!
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