Center for Conservation and Sustainable Development
• Species concepts and delimitations
• Spatial patterns of species diversity and distributions
• Ecological computer modeling
Which phenotypic characters are most useful for species delimitation? Jiménez is an Associate Scientist in the Center for Conservation and Sustainable Development. His research program seeks to determine the factors that determine the abundance and distribution of species at various spatial scales. The discovery and description of species is a major goal of systematic biology. Accordingly, much effort has been devoted to develop methods for species delimitation based on genetic data (Yang & Rannala 2010, Fujita et al. 2012, Flot et al. 2015, Jones 2017). In contrast, there has been relatively little recent development of methods for species delimitation based on phenotypic data. Yet, phenotypic data is just as important as genetic data for species delimitation (Freudenstein et al. 2016, Sukumaran & Knowles 2017, Barraclough 2019). A fundamental issue regarding species delimitation based on phenotypic data is the selection of characters. Typically, studies of species delimitation are based on an initial set of phenotypic characters, selected based on informal observations and conjectures. But whether all characters in this initial set should be retained in formal analysis for species delimitation remains an open question. The recent application of normal mixture models (NMMs) to species delimitation (Cadena et al. 2018) points at a possible approach to address this question. In particular, variable-selection algorithms for NMMs (Raftery and Dean 2006, Maugis et al. 2009a, Maugis et al. 2009b) may be useful to select variables for species delimitation, with no a priori assignment of individuals to species. However, the performance of these algorithms in the context of species delimitation has not been thoroughly explored. In this REU project one student will examine the performance of variable-selection algorithms for NMMs as applied to species delimitation based on phenotypic data. The student participating in this project will use the R environment (http://www.r-project.org/) to perform computer simulations, manipulate data and perform statistical analyses. No previous experience with statistics or the R environment is needed; but if the student is not familiar with the R language or basic statistics, strong disposition to learn a computer language and statistics is required.
• Barraclough, T.G., 2019. The Evolutionary Biology of Species. Oxford University Press.
• Cadena, C.D., Zapata, F. & Jiménez, I. 2018. Issues and Perspectives in Species Delimitation using Phenotypic Data: Atlantean Evolution in Darwin's Finches. Systematic Biology 67: 181–194.
• Flot, J.F. 2015. Species delimitation's coming of age. Systematic Biology, 64: 897–899.
• Freudenstein, J.V., Broe, M.B., Folk, R.A. & Sinn, B.T. 2016. Biodiversity and the species concept—lineages are not enough. Systematic Biology 66: 644–656.
• Fujita M.W., Leaché A.D., Burbrink F.T., McGuire J.A. & Moritz C. 2012. Coalescent-based species delimitation in an integrative taxonomy. Trends in Ecology and Evolution 27: 480–488.
• Jones, G. 2017. Algorithmic improvements to species delimitation and phylogeny estimation under the multispecies coalescent. Journal of Mathematical Biology 74: 447–467.
• Maugis C., Celeux G.,Martin-MagnietteM.-L. 2009a.Variable selection for clustering with Gaussian mixture models. Biometrics 65:701–709.
• Maugis C., Celeux G.,Martin-MagnietteM.-L. 2009b.Variable selection in model-based clustering: a general variable role modeling. Comput. Stat. Data Anal. 53:3872–3882.
• Raftery A.E., Dean N. 2006. Variable selection for model-based clustering. J. Am. Stat. Assoc. 101:168–178.
• Sukumaran, J. & Knowles, L.L. 2017. Multispecies coalescent delimits structure, not species. Proceedings of the National Academy of Sciences 114: 1607–1612.
• Yang Z. & Rannala, B. 2010 Bayesian species delimitation using multilocus sequence data. Proceedings of the National Academy of Sciences 107: 9264–9269.