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Evolution

Code: 101961
Credits: 6
2026/2027
Degree programme Type Course
Genetics OB 3

Contact lecturer

Name :
Francisco José Rodriguez-Trelles Astruga
Email :
franciscojose.rodrigueztrelles@uab.cat

Group languages

You can consult this information at the end of the document.

Prerequisites

“Nothing in biology makes sense except in the light of evolution”. This sentence by Theodosius Dobzhansky (The American Biology Teacher 1973; 35:125-129) encapsulates the role of Evolutionary Theory as unifying principle in biology.  Evolutionary Analysis integrates and therefore requires knowledge from all biological sciences.  To follow the course, it is advised to come in with a prior basic background in:

  • Transversal math and biometrical skills (basic linear algebra and calculus, randomness and probability, random variable, discrete and continuous variables, mathematical model, distribution function, stochastic process, binomial and multinomial distributions, geometric and exponential distributions, Poisson distribution, chi-square distribution, normal distribution, populations and samples, parameters and statistics, measures of central tendency and dispersion, measures of relationship, correlation and causation, statistical inference, sampling error, bias and dispersion, null hypothesis, test of hypothesis, confidence interval, significance level, experimental error, experimental design, replication, non-parametric approximation, pseudoreplication, simulation, Bayesian approximation), learnt in the degree subjects of Mathematics (1st course, 1st semester) and Biostatistics (2nd course, 1st semester).
  • Understanding of metabolism, physiology, anatomy and taxonomy of procaryote and eucaryote cells and organisms, and of fundamental concepts of classical genetics (gene, allele, homozygous and heterozygous,genotype and phenotype, asexual and sexual reproduction, somatic and germinal lines, mitosis and meiosis, gametes and genotypes, recessivity and dominance, codominance, allele segregation at one locus and at multiple loci, linkage and recombination); molecular genetics (molecular characters, nucleic acids structure, gene concepts, structural and functional categories of genomic sequences, origin and types of genetic changes, structure of regulatory regions, physical and chemical properties of amino acids, protein amino acid composition and structure, genetic codes, levels of genetic code degeneracy, mechanisms of patterning and morphogenesis, gene expression, genetic basis of development, feedback loops, epigenetics); population genetics (individuals and populations, variability, Hardy-Weinberg equilibrium, deviations from random mating, sources of genetic variation, census and effective population size, mechanisms of the evolutionary process, mutation, genetic drift, migration and gene flow, natural selection, sexual selection, adaptation, fitness and fitness components, polymorphism, substitutions and replacements, genetic load, linkage disequilibrium, genetic interaction, epistasis, adaptive landscape); quantitative genetics, (resemblance between relatives, monogenic and polygenic traits, components of phenotypic variance, additive and dominant genetic effects, heritability, selection differential, response to selection, genotype-environment interaction, nature versus nurture, genetic background, reaction norm, conflicts and trade-offs); and ecology (environment, energy flow, niche and habitat, life cycle, K and r reproductive strategies, demographic structure, population growth model, carrying capacity, survival curve, acclimation, competitive exclusion, competition and symbiosis, conflict and cooperation, trophic levels, dispersal, metapopulation, community, ecosystem, ecological network, homeostasis, resilience, robustness, ecotone, spatiotemporal patterns of diversity) learnt in the degree subjects of Microbiology (1st course, 1st semester), Animal and Plant Biology (1st course, 2nd semester), Biochemistry (1st course, 2nd semester), Genetics (1st course, 2nd semester), Molecular Genetics of Procaryotes and Eukaryotes (2nd course, 1st semester), Cytogenetics (2nd course, 1st semester), Ecology (2nd course, 1st semester), Developmental Biology (2nd course, 2nd semester), Population Genetics (2nd course, 2nd semester), and Animal Physiology (2nd course, 2nd semester).

Objectives

The theory of evolution by natural selection represents perhaps the greatest intellectual revolution experienced by mankind (Ernst Mayr. 2001. What Evolution Is. New York: Basic Books).

  • To arouse an “intense” interest in evolution as an overarching explanatory framework of the natural world and of our place in it.
  • To provide a solid understanding of the modern theory of evolution, and how this knowledge came to be through creativity, rigorous scientific method and the collaborative effort of scientists around the world, within changing socio-cultural contexts.
  • To promote awareness towards the manifold philosophical and social implications of the evolutionary thought.
  • To confront the student to the uncertainty associated with complexity and the multiple perspectives of reality, against which there are usually no unique answers.
  • To promote tolerance against ambiguity and the diverse styles of learning-to-learn and to deepen in the meaning of reality.
  • To promote intellectual autonomy in the search and acquisition of knowledge.
  • To transmit a constructive critical stance towards alternative explanations, permanently questioning any statement and, in general, any knowledge in the light of the underlying intentions and interests.
  • To translate theoretical knowledge into practice, demonstrating the applicability of evolutionary science and the positive impact that responsible citizens equipped with this knowledge can have in society.

Learning outcomes

  • CM15 (Integrate the principles of evolutionary, population, and quantitative genetics for the resolution of complex biological problems and genetic improvement.) Integrate the principles of evolutionary, population, and quantitative genetics for the resolution of complex biological problems and genetic improvement.
  • CM16 (Assess the biological significance and limitations of the results obtained in the analysis of genetic variability, quantitative inheritance and evolution.) Assess the biological significance and limitations of the results obtained in the analysis of genetic variability, quantitative inheritance and evolution.
  • CM17 (Integrate statistical and bioinformatics tools in the estimation of quantitative parameters and the inference of evolutionary processes.) Integrate statistical and bioinformatics tools in the estimation of quantitative parameters and the inference of evolutionary processes.
  • KM12 (Relate the evidence of evolutionary theory to the principles of molecular evolution and the genomic basis of adaptation.) Relate the evidence of evolutionary theory to the principles of molecular evolution and the genomic basis of adaptation.
  • SM13 (Perform computational and mathematical modelling techniques for the estimation of genetic parameters and the simulation of evolutionary processes.) Perform computational and mathematical modelling techniques for the estimation of genetic parameters and the simulation of evolutionary processes.
  • SM14 (Interpret patterns of genomic and phenotypic variation to infer the evolutionary history and genetic basis of complex traits. .) Interpret patterns of genomic and phenotypic variation to infer the evolutionary history and genetic basis of complex traits. .

Contents

Outline of the course


Lectures:


  1. Evolution explanation and language.
  2. The evidence for evolution.
  3. History of evolutionary thought
  4. Molecular evolution
  5. Phylogeny and the timing of evolutionary events.
  6. Species and speciation
  7. Radiation and extinction


Theory seminars:


  1. Origin of life
  2. Human evolution
  3. The diversity of human populations
  4. Evolution of the brain and language
  5. Evolution and health
  6. Conflict and cooperation


Learning activities and methodology

Title Hours ECTS Learning outcomes
Critical reading of prescribed texts 20 0.8 CM15, CM16, CM17, KM12, SM13, SM14
Study 60 2.4 CM15, CM16, CM17, KM12, SM13, SM14
Theory / Problems seminars 15 0.6 CM15, CM16, CM17, KM12, SM13, SM14
Bibliographical searches 12 0.48 CM15, CM16, CM17, KM12, SM13, SM14
Theory Lectures 30 1.2 CM15, CM16, CM17, KM12, SM13, SM14
Tutorials 6 0.24 CM15, CM16, CM17, KM12, SM13, SM14

The course is based on continuous assessment, with an emphasis placed on the acquisition of both knowledge and skills.  Student participation, not fearing to ask for assistance or clarification is highly encouraged and valued.

Learning activities will consist of:

Directed

  • Visually supported lectures
  • Theory seminars

Supervised

  • Individual tutoring support
  • Students presentations
  • Group work
  • In-class debates

Students' autonomous study

  • Critical reading of prescribed texts

 

Annotation: within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.

Assessment

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
First partial test 35% 3.5 0.14 CM15, CM16, CM17, KM12, SM13, SM14
Second partial test 35% 3.5 0.14 CM15, CM16, CM17, KM12, SM13, SM14
Critical reading of prescribed texts 30% 0 0 CM15, CM16, CM17, KM12, SM13, SM14

Assessment activities

  • Partial written exam 1 (35%).
  • Partial written exam 2 (35%)
  • Theory seminars, student presentations, group work and in-class debates (30%).

Note

  • To pass a partial exam, students must obtain a grade equal or superior to 5. Failing a written exam implies a FAIL for the whole course.
  • The grade for the written exam activity is the average of the grades for the two partial exams.
  • Students who obtain a grade equal or superior to 4 in a partial exam can compensate if the average with the grade of the other partial exam is equal or superior to 5.
  • Students who fail or do not attend a partial exam must attend the re-assessment exam to pass the subject (see third point in the Re-assessment section below).

Re-assessment

  • Students will re-assess only those written exams that they did not pass.
  • The maximum grade that can be awarded at re-assessment is PASS.
  • To be eligible for the re-assessment process, students should have been previously evaluated in a set of activities equaling at least two thirds of the final score of the course or module. Thus, students will be graded as \"No Avaluable\" if the weighthin of all conducted evaluation activities is less than 67% of the final score

The restricted use of artificial intelligence (AI) tools is permitted exclusively as a methodological and technical support resource throughout the learning process. Students may employ AI for literature searches, stylistic correction and text translation, initial conceptualization and brainstorming, as well as for assistance in debugging programming code and guidance in selecting statistical tests or developing data analysis software routines. However, delegating core cognitive and analytical tasks to AI is strictly prohibited; this includes writing entire sections of the assignment, designing the complete algorithmic logic, or interpreting results and conclusions. Any use of these tools must be explicitly and unequivocally declared in a methodological transparency appendix for each academic submission, detailing the tool used and the prompts employed. The absence of this declaration or the use of AI outside the permitted cases will be considered a breach of the authorship and originality criteria of the course.

The commission of any irregularity in an assessment activity (academic fraud, plagiarism, or misuse of AI technologies, unless such use is expressly authorized in the course syllabus) that could lead to a significant variation in the grade will result in a mark of 0 for that specific activity. In the event that the course syllabus stipulates a minimum grade in that assessment activity as an essential requirement to pass the course, or if multiple irregularities occur across different assessment activities within the same course, the final grade for the course will be 0. Independent of this, disciplinary proceedings may be initiated against any student who commits any of these irregularities.

This subject does not provide for the single evaluation system.

Bibliography

The basic textbooks for this subject are:

  • Futuyma D and Kirkpatrick M. 2022. Evolution (5th ed.). Sinauer.
  • Graur D. 2015. Molecular and Genome Evolution (1st ed.). Sinauer.

Complementary textbooks:

  • Baum D. A. and Smith S. D. (2012). Tree Thinking: An Introduction to Phylogenetic Biology. W. H. Freeman.
  • Cutter A. D. (2019). A Primer of Molecular Population Genetics. Oxford University Press.
  • Hamilton M. B. (2021). Population Genetics (2nd ed.). Wiley-Blackwell.

Other readings:Bettinger B. T. (2019). The Family Tree Guide to DNA Testing and Genetic Genealogy. Family Tree Books.

  • Boomsma J. J. (2023). Domains and Major Transitions of Social Evolution. Oxford University Press.
  • Bohannon C. (2023). Eve: How the Female Body Drove 200 Million Years of Human Evolution. Vintage.
  • Delisle R. G. and Tierney J. (2023). Rereading Darwin’s Origin of Species: The Hesitations of an Evolutionist. Bloomsbury Academic.
  • Graves Jr J. L. and Goodman A. H. (2021). Racism, not race : answers to frequently asked questions. Columbia University Press.
  • Harris, E. E. (2015). Ancestors in Our Genome. The New Science of Human Evolution. Oxford University Press.
  • Higham T. (2022). The World Before Us: How Science is Revealing a New Story of Our Human Origins. Penguin.
  • Jobling M., Henn, B., Hollox, E., Kivisild, T., Pagani, L., Tyler-Smith, C. (2026). Human Evolutionary Genetics. CRC Press
  • Prothero D. R. (2018). The Story of Life in 25 Fossils: Tales of Intrepid Fossil Hunters and the Wonders of Evolution. Columbia University Press.
  • Reich, D. (2018). Who We Are and How We got Here: Ancient DNA and the New Science of the Human Past. Pantheon.
  • Sommer M. (2024). The Diagrammatics of “Race”: Visualizing Human Relatedness in the History of Physical, Evolutionary, and Genetics Anthropology, ca. 1770-2020. OpenBook.
  • Stearns S. C. and Medzhitov R. (2024). Evolutionary Medicine. (2nd ed.). Oxford University Press.

Online Resources on Evolutionary Biology (updated june 2026)

1) Concepts

Understanding Evolution

https://evolution.berkeley.edu/evolibrary/resourcelibrary.php

Nature Education: Evolutionary Genetics

https://www.nature.com/scitable/topic/evolutionary-genetics-13/

European Bioinformatics Laboratory: Introduction to Phylogenetics

https://www.ebi.ac.uk/training/online/course/introduction-phylogenetics

Nature Education: Population and Quantitavive Genetics

https://www.nature.com/scitable/topic/population-and-quantitative-genetics-21/

TalkOrigins

http://www.talkorigins.org/origins/outline.html#outline

Evolution FAQs

http://www.pbs.org/wgbh/evolution/library/faq/

2) Reading

Darwin Online

http://darwin-online.org.uk/content/frameset?itemID=F373&viewtype=side&pageseq=1

Darwin's Manuscripts

https://www.amnh.org/research/darwin-manuscripts

Darwiniana and Evolution

http://www.darwiniana.org/indexpage.html#A

Evolution: Education and Outreach

https://evolution-outreach.biomedcentral.com/

New York Times

https://archive.nytimes.com/www.nytimes.com/pages/science/sciencespecial2/index.html

3) Human Evolution

Smithsonian National Museum of Natural History; Smithsonian's Human Origins Program

https://humanorigins.si.edu/


4) Resources

National Association of Biology Teachers on Evolution

https://nabt.org/Resource-Links-Evolution

UAB Guide to Online Didactic Resources

https://ddd.uab.cat/record/224929

UAB Service LLIBRES DIGITALS A PROBA

https://mirades.uab.cat/ebs/=

NOTE: wit respect to UAB's service \"LLIBRES DIGITALS A PROBA\", it is important to be aware that, by the end of the year the UAB Libraries Service will select most consulted books to acquire them and add them to the UAB's catalogue

Exploring it is highly recommended!

Software

The practical work of Evolution is conducted in the corresponding module of the subject "Laboratorio Integrado 6", using mainly the MEGA program (https://www.megasoftware.net/).

Course groups and languages

The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE

Type of teaching Group Language Semester Shift
(TE) Theory 63 Spanish second semester morning-mixed
(PAUL) Classroom practices 631 Spanish second semester morning-mixed
(PAUL) Classroom practices 632 Spanish second semester morning-mixed