2022

  • Höhna S, Kopperud BT, Magee AF: CRABS: Congruent Rate Analyses in Birth-death Scenarios, Methods in Ecology and Evolution, in press, link
  • Catalán A, Höhna S, Lower SE, Duchen P: Inferring the demographic history of the North American firefly Photinus pyralis, Journal of Evolutionary Biology, in press, link
  • Borges R, Boussau B, Höhna S, Pereira RJ, Kosiol C: Polymorphism-aware estimation of species trees and evolutionary forces from genomic sequences with RevBayes, Methods in Ecology and Evolution, in press, link
  • Kopperud BT, Lidgard S and Liow LS: Enhancing georeferenced biodiversity inventories: automated information extraction from literature records reveal the gaps, PeerJ, 10:e13921, link
  • Barido-Sottani J, Justison JA, Borges R, Brown JM, Dismukes W, Petrucci BR, Fabreti L, Höhna S, Landis MJ, Lewis, PO, May MR, Mendes FK, Pett W, Redelings BD, Tribble CM, Wright AM, Zenil-Ferguson R and Heath TA: Lessons learned from organizing and teaching virtual phylogenetics workshops. The Bulletin of the Society of Systematic Biologists, 2022, 1(2):8425, link.
  • Szöllősi GJ, Höhna S, Williams TA, Schrempf D, Daubin V and Boussau B: Relative time constraints improve molecular dating, Systematic Biology, 2022, 71(4), 797–809, link
  • Tribble CM, Freyman WA, Landis MJ, Lim JY, Barido-Sottani J, Kopperud BT, Höhna S and May MR: RevGadgets: an R Package for visualizing Bayesian phylogenetic analyses from RevBayes, Methods in Ecology and Evolution, 2022, 13:314–323, link
  • Palazzesi L, Hidalgo O, Barreda VD, Forest F, and Höhna S: The rise of grasslands is linked to atmospheric CO2 decline in the late paleogene. Nature Communications, 2022, 13, 293, link
  • Fabreti L, Höhna S: Convergence assessment for bayesian phylogenetic analysis using mcmc simulation, Methods in Ecology and Evolution, 2022, 13:77–90, link

2021

  • Höhna S, Landis MJ and Huelsenbeck JP: Parallel power posterior analyses for fast computation of marginal likelihoods in phylogenetics, PeerJ, 9:e12438, link
  • Orsi WD, Magritsch T, Vargas S, Coskun ÖK, Vuillemin A, Höhna S, Wörheide G, D’Hondt S, Shapiro BJ, Carini P: Genome evolution in bacteria isolated from million-year-old subseafloor sediment, mBio, 2021, 12:e01150–21, link.

2020

  • Magee AF, Höhna S, Vasylyeva TI, Leaché AD, and Minin VN: Locally adaptive Bayesian birth-death model successfully detects slow and rapid rate shifts, PLoS Comp. Biol., 2020, 16(10): e1007999, link

2019

  • Catalán A, Briscoe AD and Höhna S: Drift and Directional Selection Are the Evolutionary Forces Driving Gene Expression Divergence in Eye and Brain Tissue of Heliconius Butterflies, Genetics, 2019, vol. 213 no. 2 581–594, link
  • Freyman WA and Höhna S: Stochastic Character Mapping of State-Dependent Diversification Reveals the Tempo of Evolutionary Decline in Self-Compatible Onagraceae Lineages, Systematic Biology, 2019, 68 (3), 505–519, link
  • Silvestro D, Tejedor MF, Serrano-Serrano ML, Loiseau O, Rossier V, Rolland J, Zizka A, Höhna S, Antonelli A and Salamin N: Early Arrival and Climatically-Linked Geographic Expansion of New World Monkeys from Tiny African Ancestors, Systematic Biology, 2019, 68 (1), 78–92, link
  • Hsiang AY, Brombacher A, Rillo MC, Mleneck-Vautravers MJ, Conn S, Lordsmith S, Jentzen A, Henehan MJ, Metcalfe B, Fenton I, Wade BS, Fox L, Meilland J, Davis CV, Baranowski U, Groeneveld J, Edgar KM, Movellan A, Aze T, Dowsett H, Miller G, Rios N, Hull PM. (2019) Endless Forams: >34,000 modern planktonic foraminiferal images for taxonomic training and automated species recognition using convolutional neural networks, Paleoceanography and Paleoclimatology, 2019, 34, link
  • Field DJ, Berv JS, Hsiang AY, Lanfear R, Landis MJ, Dornburg A. Timing the extant avian radiation: The rise of modern birds, and the importance of modelling molecular rate variation, PeerJ Preprints, 2019, 7, e27521v1, link

2018

  • Condamine FL, Rolland J, Höhna S, Sperling FAH and Sanmartin I: Testing the Role of the Red Queen and Court Jester as Drivers of the Macroevolution of Apollo Butterflies, Systematic Biology, 2018, 67 (6), 940–964, link
  • Martin CH and Höhna S: New evidence for the recent divergence of Devil’s Hole pupfish and the plausibility of elevated mutation rates in endangered taxa, Molecular Ecology, 2018, 27 (4), 831–838, link
  • Höhna S, Coghill LM, Mount G, Thomson R and Brown JM: P3: Phylogenetic Posterior Prediction in RevBayes, Molecular Biology and Evolution, 2018, 35 (4), 1028–1034, link
  • Freyman WA and Höhna S: Cladogenetic and Anagenetic Models of Chromosome Number Evolution: a Bayesian Model Averaging Approach, Systematic Biology, 2018, 67 (2), 195–215, link

2017

  • Höhna S, Landis MJ and Heath TA: Phylogenetic Inference using RevBayes. Current Protocols in Bioinformatics, 2017, 57:6.16.1-6.16.34, link
  • Martin CH, S Höhna, Crawford JE, Turner BJ, Richards EJ and Simons LH: The complex effects of demographic history on the estimation of substitution rate: concatenated analysis results in no more than 2-fold overestimation, Proceedings of the Royal Society B, 2017, 284: 20170537, link

2016

  • Moore BR, Höhna S, May MR, Rannala B and Huelsenbeck JP: Critically evaluating the theory and performance of Bayesian analysis of macroevolutionary mixtures. Proceedings of the National Academy of Sciences, 2016, 113 (34), 9569-9574, link
  • Höhna S, Landis MJ, Heath TA, Boussau B, Moore BR, Lartillot N, Huelsenbeck JP and Ronquist F: RevBayes: Bayesian Phylogenetic Inference Using Graphical Models and an Interactive Model-Speci cation Language, Systematic Biology, 2016, 65 (4), 726-736, link
  • Conroy CR, Patton JL, Lim M, Phuong M, Parmenter B and Höhna S: Following the rivers: historical reconstruction of California voles (Microtus californicus, Muridae, Rodentia) in the deserts of eastern California, Biological Journal of the Linnean Society, 2016, 119 (1), 80–98, link
  • Höhna S, May MR and Moore BR: TESS: an R package for efficiently simulating phylogenetic trees and performing Bayesian inference of lineage diversification rates, Bioinformatics, 2016, 32 (5): 789-791, link