If you meet the admission requirements (Step 1), have successfully submitted your online application along with all required documents (Step 2), and do not qualify for direct acceptance, you will be invited to take an Entrance Exam (Step 3).
The exam is offered both online (subject to a fee) and in person (free of charge) at the Biocenter of LMU Munich. The online version involves external service fees of approximately €70 (plus any applicable country-specific taxes), payable via credit card or PayPal. There is NO option to provide any fee waivers!
Detailed information about the exam format, exact date, and applicable fees will be included in the official invitation.
Exam Details
The entrance exam is a 90-minute, English-language, multiple-choice test. It will cover topics from the following subject areas:
- Evolution
- Ecology
- Systematics
The questions are designed at the bachelor’s level, and any standard textbook used in a bachelor’s biology program can be used for preparation.
Please note: Sample questions are not available!
Expected Knowledge
We expect that beginners in the EES program are familiar with most of the contents listed below. We are aware that many applicants for the EES program lack some of this knowledge and expect that they read up on these topics before taking the EES Entrance Exam (see the list of recommended textbooks below).
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 conceptsl.
- 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.
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.
Recommended Textbooks
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-Blackwel
- 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
After taking the Exam
Once you have completed the entrance exam, a combined score will be calculated based on your exam grade (70%) and your bachelor’s degree grade point average (30%).
- Applicants with a combined score of 2.3* or lower are accepted to the Master’s program.
- Applicants with a combined score higher than 2.93* and up to 3.0* will be invited to the final step of the admission process – the interview.
* According to the German grading scale, where 1.0 is the best and 4.0 is the minimum passing grade.