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The course intends to develop and sharpen evolutionary thinking, based on solving conceptual problems using
concrete methods and examples. The methods and examples take advantage of statistical approaches (phylogenetic comparative methods, diversification analyses, ancestral reconstructions, GIS) broadly used to address key questions in ecology and evolution (community structure, evolution of life histories, biogeography, macroevolution). Students will consequently learn how to define an evolutionary question, design the methodology for solving such question, and practically implement the solution using proper statistical tools, especially in R. The course covers five related areas: (1) Intro to evolutionary data, (2) Evolution of life-history, (3) Evolution of ecological communities, (4) Evolution of geographic patterns, (5) Evolution of higher taxa. No pre-requisite courses are required. But undergraduate-level understanding of evolution and ecology is expected. Preliminary knowledge of R will increase the benefits of taking the course, but is not needed to for its successful completion. The course is built to provide the most practical tools to address relevant biological problems, such as those commonly addressed in master’s theses and dissertations. The course runs in English, Czech, or some combination of both, depending on the language and the preferences of the students. Last update: Sacherová Veronika, RNDr., Ph.D. (30.04.2025)
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Bodega Bay workshop in applied phylogenetics (2018) UC Davis & UC Berkeley
http://treethinkers.org
Herron and Freeman (2014) Evolutionary analysis. University of Washington. || Nunn (2011) The comparative approach in evolutionary anthropology and biology. University of Chicago Press. Last update: Sacherová Veronika, RNDr., Ph.D. (30.04.2025)
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The course runs in a block of 5 days. It concludes with an examination, which has an empirical component (working on practical exercises demonstrated during the lectures) and a conceptual component (discussion over the presented material). Last update: Sacherová Veronika, RNDr., Ph.D. (30.04.2025)
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1. Intro to evolutionary data (evolution in molecular, geographic and functional data, public databases, overview of concepts and methods on phylogeny construction, compiling, manipulating and editing trees for further analysis in R, constructing supertrees and consensus trees, time-calibrating trees in R) (MRBAYES, BEAST, FigTree, R) READING: Felsenstein 2003, Donoghue and Benton 2007.
2. Evolution of phenotypes, life histories and trade-offs (models of trait evolution, ancestral reconstructions, coevolution of traits and species, rate of trait evolution, phylogenetic signal, niche conservatism, reconstruction of the niche, evolution in the niche space) (APE, GEIGER) READING: Wiens and Donoghue 2004, Felsenstein 1985.
3. Evolution of communities (species and phylogenetic diversity, community phylogenetics, overdispersion and clustering at the phylogenetic and phenotypic level, delimitation of species pools, dispersal, null models, inferring competition from community structure) (PHYLOCOM, PICANTE) READING: Webb et al. 2000, Cavender-Barres et al. 2004.
4. Evolution of geographic patterns (biogeography & macroecology) (historical biogeography, reconstruction of past dispersal, models colonization and dispersal, evolution in island biogeography) (BIOGEOBEARS, LAGRANGE, DIVA) READING: Mittelbach et al. 2007, Schluter and Pennell 2017.
5. Evolution of higher taxa (speciation formation, diversification) (genetic and ecological formation of species, speciation and extinction, diversification, background extinction, causes of mass extinctions, inferring diversification dynamics from phylogenies, ecology of the diversification process, state-dependent diversification models) (BAMM, REVBAYES, LASER) READING: Benton and Emerson 2007, Rabosky and Glor 2010. Last update: Sacherová Veronika, RNDr., Ph.D. (30.04.2025)
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The course systematically develops the student’s ability to progress from the conceptual formulation of a research question (biogeography, macroevolution, community ecology), through the selection of appropriate methods and practical data analysis, to the critical interpretation of results, in direct alignment with the structure of the teaching and the examination.
After successful completion of the course, the student:
· Formulates a clearly defined evolutionary problem in the fields of macroevolution, ecology, or biogeography and places it within the relevant contemporary theoretical framework of evolutionary biology. · Selects, justifies, and critically evaluates appropriate analytical approaches (phylogenetic comparative methods, diversification analyses, ancestral reconstructions, biogeographic models) corresponding to the given research question. · Identifies, verifies, and, if necessary, modifies a time-calibrated phylogenetic tree from available databases and prepares it for subsequent evolutionary analyses. · Integrates heterogeneous evolutionary data (phylogenies, traits, geographic distributions, environmental layers) into a unified analytical framework in the R environment. · Models the evolution of phenotypic and ecological traits, interprets phylogenetic signal, rates of evolution, coevolution, and evolutionary trade-offs, including their biological implications. · Analyzes the structure of ecological communities using phylogenetic and functional metrics, applies null models, and interprets patterns of phylogenetic and functional clustering and overdispersion. · Applies biogeographic and macroecological models to reconstruct the historical distributions of taxa and to test hypotheses of dispersal, colonization, and geographic spread. · Estimates diversification processes (speciation and extinction) from molecular phylogenies and critically evaluates the assumptions, limitations, and interpretational challenges of state-dependent diversification models. · Independently implements evolutionary analyses in the R programming language, including work with commonly used packages and specialized software. · Critically interprets the results of evolutionary analyses, distinguishes between statistical output and biological interpretation, and identifies the main sources of uncertainty and potential bias. · Critically discusses primary scientific literature in the context of the methods and theories covered and integrates data, analytical approaches, and concepts into a coherent argument. · Designs a realistic analytical workflow applicable to a master’s or doctoral thesis in evolutionary biology and macroecology.
The student will be proficient in the following tools: · R packages for evolutionary and ecological analysis (ape, geiger, phytools, picante, phylobase). · Bayesian software for phylogenetic analyses (MrBayes, BEAST, RevBayes). · Visualization, editing, and quality control of phylogenetic trees (FigTree). · Analysis of phylogenetic community structure (PHYLOCOM; R packages picante, vegan). · Historical biogeography (BioGeoBEARS, LAGRANGE, DIVA). · Diversification analyses (BAMM, RPANDA, LASER). · Work with geographic data and maps in the R environment (GIS; R packages sp, raster, terra). Last update: Sacherová Veronika, RNDr., Ph.D. (29.01.2026)
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| The course does not include work placement |