Basanta Khakurel
Doctoral candidate
I am a PhD candidate in the Department of Earth and Environmental Sciences at Ludwig Maximilians-Universität München (LMU) since 2023. I hold both a Master of Science and a Bachelor of Science in Biology from Southeastern Louisiana University. My research centers on developing and applying robust models for morphological phylogenetic inferences.
- 2021 Graduate Research Fellowship, Louisiana Biomedical Research Network
Research
My research interest focuses on developing and implementing robust statistical models for phylogenetic inferences using morphological character data. I am particularly interested in Bayesian methods and the application of complex models incorporating more biological realism, to better understand evolutionary history from morphological datasets. My work aims to improve the accuracy of evolutionary trees by addressing fundamental aspects of morphological data analysis.
Robust Models for Morphological Phylogenetics
My doctoral research focuses on improving the accuracy of phylogenetic trees inferred from morphological datasets. Morphological data is crucial for understanding the evolution of both extant and extinct species, but it presents unique analytical challenges. My work involves developing and implementing novel statistical models, such as the Covarion model, which accounts for variation in the rate of character change over time. By employing these Bayesian methods that incorporate more biological realism, this research aims to create more robust and reliable frameworks for reconstructing evolutionary history using morphological datasets. This research is funded under the ‘MacDrive’ project by European Research Council.
Khakurel, B., & Höhna, S. (2025). A covarion model for phylogenetic estimation using discrete morphological datasets. bioRxiv, 2025–06. https://doi.org/10.1101/2025.06.20. 660793
Heckeberg, N. S., Capobianco, A., Khakurel, B., Darlim, G., & Höhna, S. (2025). Practical guide and review of fossil tip-dating in phylogenetics. Systematic Biology, syaf050. https://doi.org/10.1093/sysbio/syaf050