Michael R. May

3.5k total citations
26 papers, 1.2k citations indexed

About

Michael R. May is a scholar working on Molecular Biology, Genetics and Paleontology. According to data from OpenAlex, Michael R. May has authored 26 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 14 papers in Genetics and 8 papers in Paleontology. Recurrent topics in Michael R. May's work include Genomics and Phylogenetic Studies (12 papers), Genetic diversity and population structure (10 papers) and Evolution and Paleontology Studies (8 papers). Michael R. May is often cited by papers focused on Genomics and Phylogenetic Studies (12 papers), Genetic diversity and population structure (10 papers) and Evolution and Paleontology Studies (8 papers). Michael R. May collaborates with scholars based in United States, Germany and Sweden. Michael R. May's co-authors include Brian R. Moore, Sebastian Höhna, Bruce Rannala, John P. Huelsenbeck, Jeffrey Ross‐Ibarra, John Eid, José Fernando Garcia, J. Graham Ruby, Ian Korf and Natalie Telis and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Bioinformatics and PLoS ONE.

In The Last Decade

Michael R. May

24 papers receiving 1.2k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Michael R. May United States 13 493 385 353 329 294 26 1.2k
Rezwana Reaz Bangladesh 5 608 1.2× 235 0.6× 527 1.5× 348 1.1× 148 0.5× 10 1.0k
Tanja Gernhard Germany 4 305 0.6× 179 0.5× 330 0.9× 383 1.2× 195 0.7× 5 920
Elizabeth A. Housworth United States 16 512 1.0× 317 0.8× 405 1.1× 317 1.0× 230 0.8× 32 1.3k
Théo Zimmermann United States 4 561 1.1× 227 0.6× 476 1.3× 325 1.0× 135 0.5× 9 912
Alexander S. T. Papadopulos United Kingdom 19 345 0.7× 319 0.8× 496 1.4× 505 1.5× 81 0.3× 39 1.1k
Marek L. Borowiec United States 14 380 0.8× 178 0.5× 764 2.2× 874 2.7× 143 0.5× 36 1.5k
Michael S. Brewer United States 18 310 0.6× 133 0.3× 621 1.8× 349 1.1× 130 0.4× 42 1.1k
Heath Blackmon United States 18 361 0.7× 390 1.0× 707 2.0× 294 0.9× 60 0.2× 46 1.0k
Benjamin D. Redelings United States 12 451 0.9× 167 0.4× 317 0.9× 191 0.6× 193 0.7× 24 804
Jeff J. Shi United States 7 275 0.6× 154 0.4× 302 0.9× 605 1.8× 350 1.2× 9 1.0k

Countries citing papers authored by Michael R. May

Since Specialization
Citations

This map shows the geographic impact of Michael R. May's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Michael R. May with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael R. May more than expected).

Fields of papers citing papers by Michael R. May

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael R. May. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Michael R. May. The network helps show where Michael R. May may publish in the future.

Co-authorship network of co-authors of Michael R. May

This figure shows the co-authorship network connecting the top 25 collaborators of Michael R. May. A scholar is included among the top collaborators of Michael R. May based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michael R. May. Michael R. May is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Thompson, Ammon, Michael R. May, Ben R. Hopkins, et al.. (2025). Quantifying Transcriptome Turnover on Phylogenies by Modeling Gene Expression as a Binary Trait. Molecular Biology and Evolution. 42(5).
2.
Ding, Wenna, Richard H. Ree, Michael R. May, et al.. (2025). The asynchronous rise of Northern Hemisphere alpine floras reveals general responses of biotic assembly to orogeny and climate change. Science Advances. 11(51). eadz1888–eadz1888.
3.
May, Michael R., et al.. (2025). Fossils improve extinction-rate estimates under state-dependent diversification models. Philosophical Transactions of the Royal Society B Biological Sciences. 380(1919). 20230313–20230313. 2 indexed citations
4.
May, Michael R., et al.. (2024). Assessing the Adequacy of Morphological Models Using Posterior Predictive Simulations. Systematic Biology. 74(1). 34–52. 6 indexed citations
5.
Tribble, Carrie M., José Ignacio Márquez‐Corro, Michael R. May, et al.. (2024). Macroevolutionary inference of complex modes of chromosomal speciation in a cosmopolitan plant lineage. New Phytologist. 245(5). 2350–2361. 3 indexed citations
6.
May, Michael R. & Bruce Rannala. (2024). Early detection of highly transmissible viral variants using phylogenomics. Science Advances. 10(33). eadk7623–eadk7623. 2 indexed citations
7.
Hauffe, Torsten, et al.. (2024). Challenges in estimating species' age from phylogenetic trees. Global Ecology and Biogeography. 33(10). 3 indexed citations
8.
May, Michael R., et al.. (2023). Model misspecification misleads inference of the spatial dynamics of disease outbreaks. Proceedings of the National Academy of Sciences. 120(11). e2213913120–e2213913120. 8 indexed citations
9.
May, Michael R. & Carl J. Rothfels. (2023). Diversification Models Conflate Likelihood and Prior, and Cannot be Compared Using Conventional Model-Comparison Tools. Systematic Biology. 72(3). 713–722. 10 indexed citations
10.
May, Michael R., et al.. (2022). PrioriTree: a utility for improving phylodynamic analyses in BEAST. Bioinformatics. 39(1). 3 indexed citations
11.
Tribble, Carrie M., et al.. (2022). Unearthing Modes of Climatic Adaptation in Underground Storage Organs Across Liliales. Systematic Biology. 72(1). 198–212. 9 indexed citations
12.
May, Michael R., et al.. (2022). New Phylogenetic Models Incorporating Interval-Specific Dispersal Dynamics Improve Inference of Disease Spread. Molecular Biology and Evolution. 39(8). 7 indexed citations
13.
Tribble, Carrie M., William A. Freyman, Michael J. Landis, et al.. (2021). RevGadgets: An R package for visualizing Bayesian phylogenetic analyses from RevBayes. Methods in Ecology and Evolution. 13(2). 314–323. 58 indexed citations
14.
May, Michael R., Dori L. Contreras, Michael Sundue, et al.. (2021). Inferring the Total-Evidence Timescale of Marattialean Fern Evolution in the Face of Model Sensitivity. Systematic Biology. 70(6). 1232–1255. 26 indexed citations
15.
Thompson, Ammon, Michael R. May, Brian R. Moore, & Artyom Kopp. (2020). A hierarchical Bayesian mixture model for inferring the expression state of genes in transcriptomes. Proceedings of the National Academy of Sciences. 117(32). 19339–19346. 16 indexed citations
16.
17.
Höhna, Sebastian, Michael R. May, & Brian R. Moore. (2015). TESS: an R package for efficiently simulating phylogenetic trees and performing Bayesian inference of lineage diversification rates. Bioinformatics. 32(5). 789–791. 104 indexed citations
18.
Magee, Andrew F., Michael R. May, & Brian R. Moore. (2014). The Dawn of Open Access to Phylogenetic Data. PLoS ONE. 9(10). e110268–e110268. 35 indexed citations
19.
Melters, Daniël P., Keith Bradnam, Hugh A. Young, et al.. (2013). Comparative analysis of tandem repeats from hundreds of species reveals unique insights into centromere evolution. Genome biology. 14(1). R10–R10. 348 indexed citations
20.
May, Michael R., et al.. (2009). A Pleistocene Clone of Palmer's Oak Persisting in Southern California. PLoS ONE. 4(12). e8346–e8346. 25 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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