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Taxonomy lays the foundations for the study of biodiversity and its conservation. Procrustean geometric morphometrics (GMM) is a most common technique for the taxonomic assessment of phenotypic population differences. To measure biological variation and detect evolutionarily significant units, GMM is often used on its own, although it is much more powerful with an integrative approach, in combination with molecular, ecological and behavioural data, as well as with meristic morphological traits. GMM is particularly effective in taxonomic research, when applied to 2D images, which are fast and low cost to obtain. Yet, taxonomists who may want to explore the usefulness of GMM are rarely experts in multivariate statistical analyses of size and shape differences. In these twin papers, I aim to provide a detailed step-by-step guideline to taxonomic analysis employing Procrustean GMM in user-friendly software (with tips for R users). In the first part (A) of the study, I will focus on preliminary analyses (mainly, measurement error, outliers and statistical power), which are fundamental for accuracy, but often neglected. I will also use this first paper, and its appendix (Appendix A), to informally introduce, and discuss, general topics in GMM and statistics, that are relevant to taxonomic applications. In the second part (B) of the work, I will move on to the main taxonomic analyses. Thus, I will show how to compare size and shape among groups, but I will also explore allometry and briefly examine differences in variance, as a potential clue to population bottlenecks in peripheral isolates. A large sample of North American marmot mandibles provides the example data (available online, for readers to replicate the study and practice with analyses). However, as this sample is larger than in previous studies and mostly unpublished, it also offers a chance to further explore the patterns of interspecific morphological variation in a group, that has been prominent in mammalian sociobiology, and whose evolutionary divergence is complex and only partially understood.
In this study, we perform a quantitative assessment of the role of money as an indicator variable for monetary policy in the euro area. We document the magnitude of revisions to euro area-wide data on output, prices, and money, and find that monetary aggregates have a potentially significant role in providing information about current real output. We then proceed to analyze the information content of money in a forward-looking model in which monetary policy is optimally determined subject to incomplete information about the true state of the economy. We show that monetary aggregates may have substantial information content in an environment with high variability of output measurement errors, low variability of money demand shocks, and a strong contemporaneous linkage between money demand and real output. As a practical matter, however, we conclude that money has fairly limited information content as an indicator of contemporaneous aggregate demand in the euro area.