🦇🧬Congratulations to E & E alum Bill R. Thomas, whose collaborative work upended bat biogeography in a major new Nature study from the @ bat1kgenomes.bsky.social !
👉 Check it out!
doi.org/10.1038/s415...
Dávalos Lab
@labdavalos.bsky.social
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Principal Investigator 👉🏽 lmdavalos.github.io Editor-in-Chief 👉🏽 @qrb.bsky.social/ recent Fulbright Amazonia scholar (www.fulbrightprogram.org/amazonia), news from lab and beyond
![A summary of the methods and main results. We inferred transition rates between leaf shapes in two different ways: (a) by fitting a continuous time Markov chain (CTMC) model of trait evolution to phylogenetic trees and (b) by simu- lating mutation in a computational model of leaf development. (a) (Left) A sample of images from the Naturalis Biodiversity Center [31][32–35] herbarium are manually classified as unlobed (u), lobed (l), dissected (d) or compound (c) (Methods sec- tion IV F). This dataset, as well as an analogous dataset by Geeta et al. [36], are used to label the tips of 5 different phylogenetic trees representing angiosperm evolution [36–38]. (Centre) the Zuntini et al. [38] species tree is shown in full. Rates are inferred by fitting a CTMC model to the labelled trees using a Markov chain Monte Carlo (MCMC) approach in BayesTraits V4.1.2 [39]. (Right) Posterior net rates for the Zuntini et al. [38] species tree are shown as arrows. Black (grey) arrows depict rates for which 0 falls outside (within) a 95% credible interval. (b) (Left) An initial sample of 48 leaves representing a wide morphospace is generated using the Runions et al. [14] model (Methods sections IV A and IV B). Leaves in this sample are mutated in parallel through a random walk algorithm, with no selection besides rejecting parameter combinations that give physically implausible leaves (table I), generating a sequence of leaves that evolve away from the initial leaf (Methods section IV C). (Middle) Every leaf is classified as unlobed (u), lobed (l), dissected (d) or compound (c) using an automated morphometric approach (Methods section IVD), giving a sequence of character states for each walk. To this trajectory, a CTMC model is fitted using MCMC (Methods section IV E). (Right) Arrows depict posterior net rates for the MUT2 scheme (alg. 1).](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:qp7j4izlc5kxl4vtdzxl4gsx/bafkreic7l4ox4p52vvqkxrshtqbnrpwdsojetgrh7myo5wfnvcaibn7jzu)
