Multivariate Morphometrics

Multivariate Morphometrics

Morphometrics

Morphometrics is one of the most dynamic and popular fields on the contemporary biological scene. Focusing, as it does, on the quantitative characterization and analysis of morphological data, students increasingly see morphometrics as a necessary complement to molecular studies in their quests to understand the origin and maintenance of biodiversity. Moreover, morphometrics has recently been shown to have direct utility in phylogenetic contexts, by both finding new, and sharpening the definition of old, character states. At this juncture in the field’s development, a more up-to-date and thorough treatment of the use of morphometric procedures in a wide variety of contexts is needed. The book in hand, Morphometrics provides such answers to real-world questions for real-world systematists.

Morphometrics with R

This book aims to explain how to use R to perform morphometrics. Morpho- tric analysis is the study of shape and size variations and covariations and their covariations with other variables. Morphometrics is thus deeply rooted within stat- tical sciences. While most applications concern biology, morphometrics is becoming common tools used in archeological, palaeontological, geographical, or medicine disciplines. Since the recent formalizations of some of the ideas of predecessors, such as D’arcy Thompson, and thanks to the development of computer techno- gies and new ways for appraising shape changes and variation, morphometrics have undergone, and are still undergoing, a revolution. Most techniques dealing with s- tistical shape analysis have been developed in the last three decades, and the number of publications using morphometrics is increasing rapidly. However, the majority of these methods cannot be implemented in available software and therefore prosp- tive students often need to acquire detailed knowledge in informatics and statistics before applying them to their data. With acceleration in the accumulation of me- ods accompanying the emerging science of statistical shape analysis, it is becoming important to use tools that allow some autonomy. R easily helps ful?ll this need. Risalanguage andenvironment forstatisticalcomputingandgraphics. Although there is an increasing number of computer applications that perform morphometrics, using R has several advantages that confer to users considerable power and possible new horizons in a world that requires rapid adaptability.