Overview

"Nothing in biology makes sense except in the light of evolution”

Theodosius Dobzhansky (1973)

 

Where does the diversity of earth's organisms come from? How did this diversity develop? These are central questions in evolutionary biology. Since the 19th century we know that all living things share a common ancestor, and that species have changed - and still change - over time.

The science of phylogenetics tries to reconstruct the evolutionary history of organisms. Which organisms are closely related? What do we know about their common ancestor. What happened on the way from the ancestor to the specimen we can observe know? These questions are difficult to answer. Fossils that can tell us about the past are extremely rare, and for many groups of smaller and inconspicuous organisms they do not exist at all. This means for reconstructing the development that leads to today's diversity, we have to rely on what characters we can observe on today's organisms. These can be morphological or anatomical features, ecological, behavioral, or even geographical characteristics. But often many species are very small and do not exhibit many features (think of the many fungi that are only known as colorless hyphae), or the organisms of interest are so closely related that they are are hardly distinguishable. It was not until the usefulness of molecular characters for phylogenetic reconstruction was discovered that scientists were able to establish reasonable hypotheses for the evolution of some groups (Fungi again...).

However, although molecular data (usually information from DNA sequence data, ACGTGGAC....) does provide us with lots of information, it does not necessarily make things easier. Many of the problems we have with 'classical' morphological data are still present, but, due to the large amount of data (often thousands and ten thousands of characters for hundreds or sometimes thousands of organisms), on a much larger scale. To overcome these problems, sophisticated mathematical models and computer software together with large amounts of computational power are essential - analyses that run for days on more than a hundred CPUs are not uncommon.