Hola! I am delighted to announce that I have been invited to present a seminar in Madrid at the Complutense University on Monday 17th November 2014. I am being hosted by Dr. Diego San Mauro of the Department of Zoology and Physical Anthropology. The seminar is open to staff and students of the University as well as those affiliated with the Madrid's Museo Nacional de Ciencias Naturales (Museum of Natural Sciences) and any other academics of Madrid's various institutes. 

Macroevolution in the Underworld: large-scale patterns of morphological evolution in burrowing animals
Burrowing underground is a common life strategy in the animal kingdom, providing protection from changing climates and predators. I shall give an overview of my research into the morphological evolution of burrowing animals – predominantly limbless vertebrates, but with a brief diversion into molluscs. In doing so, I shall demonstrate the cutting-edge techniques I develop and apply to address classical questions of macroevolution.

I shall be presenting at the new Conference on Modern Phylogenetic Comparative Methods and their Application in Evolutionary Biology in Seville, Spain in November 11-15th 2014. Abstract below.
In this talk I shall demonstrate with some interesting biological examples, the various methods now available to combine shape data with phylogenetic comparative methods, all of which are implemented in our software package Geomorph. Should be a great meeting. Hope to see you there!

Geomorph: Uniting Phylogenetic Comparative Biology with High-Dimensional Data

Emma Sherratt 1, Michael L. Collyer2 and Dean C. Adams3
1 School of Environmental and Rural Sciences, University of New England, Armidale NSW Australia
2 Department of Biology, Western Kentucky University, Bowling Green, KY, USA
3 Department of Ecology, Evolution, and Organismal Biology, Department of Statistics, Iowa State University, Ames, IA, USA

Studies of evolutionary correlations commonly utilise phylogenetically independent contrasts or phylogenetic generalised least squares to assess trait covariation in a phylogenetic context. However, while these methods are appropriate for evaluating trends in one or a few traits, they are incapable of assessing patterns in highly-multivariate data, as the large number of variables relative to sample size prohibits the algebra from being completed. This poses serious limitations for comparative biologists, who must either simplify how they quantify phenotypic traits, or alter the biological hypotheses they wish to examine. Geomorph (www.geomorph.net) is an established package in the statistical environment R (CRAN) that provides functions to analyse high-dimensional data in a phylogenetic context. Here I shall demonstrate several geomorph analyses using example datasets comprising landmark-based morphometric data: ANOVA and regression models for analysing trait covariation with continuous and discrete variables, phylogenetic two-block partial least squares analysis for covariation between sets of traits, estimating the rate of morphological evolution in multivariate datasets, and a generalised K statistic for estimating phylogenetic signal. Together, these functions provide an operational platform to enable macroevolutionary biologists to test hypotheses of adaptation and phenotypic change in high-dimensional datasets. 


And for those interested in the methods, the following papers describe them:

Adams, D.C. 2014. A method for assessing phylogenetic least squares models for shape and other high-dimensional multivariate data. Evolution. 68:2675-2688. [PDF]

Adams, D.C. 2014. A generalized K statistic for estimating phylogenetic signal from shape and other high-dimensional multivariate data. Systematic Biology. 63:685-697. [PDF]

Adams, D.C. and R. Felice. 2014. Assessing phylogenetic morphological integration and trait covariation in morphometric data using evolutionary covariance matrices. PLoS ONE. 9(4):e94335. [PDF]

Adams, D.C. 2014. Quantifying and comparing phylogenetic evolutionary rates for shape and other high-dimensional phenotypic data. Systematic Biology. 63:166-177. [PDF]