Munich Graduate Program for Evolution, Ecology and Systematics
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Expected knowledge for the EES Master's program

We expect that beginners in the EES program are familiar with most of the contents listed below. We are aware that many applicants for admission to the EES program lack some of this knowledge and expect that they read up on these topics before taking the EES admissions exam.

Evolution

  • Evolutionary Biology: Darwin’s theory of evolution by natural selection.
  • Evidence for the evolution of organisms from common ancestors
  • Understanding the differences in the way of thinking of Cuvier, Lamarck, and Darwin, and their arguments
  • Definitions of evolution; micro-evolution; macro-evolution
  • Evolutionary Processes: Basic understanding of mutation, selection, genetic drift, recombination, migration
  • The principle underlying sexual selection and sexual conflict
  • Homology, analogy and convergent evolution: well-corroborated examples for all three concepts
  • Geographic scenarios for the formation of species: allopatry, parapatry, sympatry

Ecology

Environmental factors and resources, ecosystems

  • Environmental factors and resources: ecological niche (n dimensional); competition (inter, intra), stoichiometry
  • Ecology of different habitats (structure and function), biomes, biochemical cycles,
  • Ecological concepts and principles (minimum population, disturbances, resilience…)

Individuals, populations, communities:

  • Mutualism, altruism, symbioses, commensalism
  • Metapopulation, food web interactions, key stone species, key stone ecosystems, key stone mechanisms, bottom-up top-down control.
  • Biodiversity, paradox of enrichment, intermediate disturbance hypothesis, island theory.
  • Understanding of ecological relations and ecological models
  • Population biology including a basic understanding of fecundity, mortality and life history traits

Behavioural and Evolutionary ecology

  • Comparative versus experimental approaches
  • Tinbergen’s four questions, proximate versus ultimate explanations
  • Optimality theory, optimal foraging, reaction norms, trade-offs, constraints
  • Evolutionary arms races, resource competition, living in groups, territoriality, sexual selection and sexual conflict, parental care and family conflict, mating systems, sex allocation, social behaviour, kin selection, cooperation, altruism and conflict, communication and signals
  • Animal communication and social structure.
  • Carrying capacity, life-history strategies, trade-offs, population growth, fitness,
  • Interactions of organisms with the environment (abiotic, biotic), feeding strategies: grazers, carnivorous, parasites; predator prey model, functional responses.

Systematics

  • Fundamental principles of systematics, including species concepts, speciation, extinction biogeography and nomenclature.
  • Species relatedness through descent from a common ancestor (“tree thinking”).
  • Approaches of phylogeny reconstruction
  • Cladistics and classification concepts
  • Rough overview of the phylogeny of multicellular organisms (animals, plants, fungi)
  • The role of the fossil record in evolutionary biology and systematics

Expected background knowledge from other fields

Molecular Genetics

  • Major biological macromolecules (e.g. DNA, RNA, protein). The central dogma of molecular biology (e.g. transcription, translation). Degeneracy of the genetic code.
  • DNA as the repository of genetic information; understanding the roles of DNA and RNA
  • Understanding the experiment of Meselson and Stahl; the complementary of nucleic acids on opposite complementary DNA or RNA strands that are connected via hydrogen bonds; the canonical Watson-Crick base pairing; DNA replication
  • Protein biosynthesis; redundancy of the genetic code; transcription and its regulation; translation
  • The difference between mutation and substitution; DNA repair

Genomics

  • What is a genome?
  • Basic organizational structure of genomes.
  • What is a typical size for a mammalian genome (in base pairs of DNA)? How many protein-coding genes are in a typical mammalian genome?
  • What is a transcriptome?

Mendelian Genetics

  • Mendel’s laws of segregation and independent assortment.
  • What is a Mendelian trait? What are alleles?
  • What is a homozygote/heterozygote? What is dominance/recessivity?
  • What is a genotype/haplotype?

Quantitative Genetics

  • Genetic and environmental variance
  • What is a quantitative trait?
  • What are additive genetic effects? What is heritability? What is epistasis?

Cell Biology

  • Basic cell biological principles including compartmentation, cell division, replication, mitosis, meiosis, etc.

Statistics and Probability Theory

  • Thorough understanding and ability to apply concepts from basic probability theory such as inclusion-exclusion formula, stochastic independence, Bayes formula, binomial distributions and their approximation by normal distributions and basic combinatorics such as n! (“n factorial”) and “n choose k”.
  • Expectation values / mean values, standard deviations, variances, correlations, standard errors (of sample means): How to calculate them from samples/data, how to interpret them, how to estimate them from scatter plots.
  • Interpretation of histograms (also when they show densities instead of numbers), scatter plots, boxplots.
  • Principles of statistical testing, including the exact meaning of the following concepts: null hypothesis, test statistic, significance level, p-value, multiple-testing correction.
  • Understanding of t-tests (one- or two sided, paired or unpaired, why using Student’s t-distribution and not just the normal distribution to assess significance of the t-test), chi-square tests (goodness-of-fit and tests of homogeneity/independence) and one-factor anova: When to apply these tests, structure of their test statistics, distribution assumptions. How to use quantile tables to assess significance when applying these tests.
  • For the basic non-parametric tests Wilcoxon/Mann-Whitney and Kruskal-Wallis: Underlying ideas and conditions under which these tests could or should be applied.
  • Linear regression with one explanatory variable: How to make predictions based on a linear regression model, relationship between the slope and correlation, underlying assumptions in linear regression analyses and how to check whether the assumptions are fulfilled using quantile-quantile-plots.

Examples of books to read up about these contents

Below are some examples of text books for reading up some of the contents listed above. Of course, other books or online resources may also be helpful.

  • Urry, L.A., Cain, M.L., Wasserman, S.A., Minorsky, P.V., and Reece, J.B. (2016) Campell Biology (11th Edition)
  • Barton, Briggs, Eisen, Goldstein, and Patel (2007) Evolution; Cold Spring Harbor Laboratory Press.
  • Futuyma (2013) Evolution (3rd ed.); Sinauer
  • Begon, M., Townsend, C.A. and Harper, J.L (2005). Ecology: From Individual to Ecosystems (4th Edition), Blackwell Publishing
  • Hartl and Cochrane (2017) Genetics: Analysis of Genes and Genomes (9th ed.); Jones and Bartlett.
  • Davies, N.B., Krebs, J.R. and West, S.A. (2012). An Introduction to Behavioural Ecology (4th Edition), Wiley-Blackwell
  • Sokal, Rohlf (2009) Introduction to Biostatistics, 2nd Ed.; Dover Publications
  • Freedman, Pisani, Purves (2007) Statistics, 4th Ed.; Norton & Company
  • Shahbaba (2012) Biostatistics with R; Springer

What will be taught in the EES program?

Students in the EES program specialize in a particular research area. Here are some examples of what is taught in various EES-modules:

  • DNA and protein sequence evolution, molecular clock
  • Using sequence data to infer species relationships (molecular phylogenetics)
  • Mathematically explicit formulation of evolutionary processes
  • Genetic variation within species, statistical tests for adaptive molecular evolution
  • Human evolution, divergence from other primates, migration patterns
  • Evolutionary developmental biology (Evo-Devo)
  • Designing ecological experiments in natural or semi-natural conditions
  • Genome evolution and the evolution of sex chromosomes
  • Handling of large-scale genomic data
  • Conceptual understanding of fundamental evolutionary processes such as adaptation, speciation and sexual reproduction
  • Statistical modeling and calculating with random variables and their expectation values, variances and covariances, e.g. in quantitative genetics
  • Generalized linear models of type Poisson, logistic regression and mixed-effects models, and how these methods and concepts are applied e.g. in ecology
  • How to do all this efficiently with the software R (see http://www.r-project.org/ ).
  • Principles of Bayesian and frequentistic statistics, e.g. in phylogenetics

Furthermore, EES students are trained in applying this knowledge when carrying out their research projects.