Instructor(s): D. Jackson

Course Description: Biologists are often faced with complex datasets having numerous observations and many variables, with these variables often being associated (e.g. correlated) with each other. Multivariate statistical analyses allow us to explore such datasets to determine the underlying relationships (e.g. which observations are most similar, or how are different sets of variables related?) and to test hypotheses. This course provides an introduction to the diverse set of multivariate statistical approaches. Comparisons of the influence of choices in resemblance measures and data form will be considered. Ordination methods such as principal component analysis, principal coordinates analysis and non-metric multidimensional scaling, and cluster analyses will be examined. Methods to permit hypothesis tests such as redundancy analysis will be introduced. A major goal of the course is to introduce students to the diversity of methods and the choices available, while emphasizing the importance these choices may have on your results and interpetation. The major assignment is a research project undertaken using student datasets to evaluate a set of these comparisons.

Location: St. George, room: ESC 2144
Date & Time: Tuesday: 10:00am-1:00pm, (Jan. 7 – Apr. 22)

Instructor(s): H. Rodd + N. Rollinson

Course Description: This course is recommended for students just starting an MSc or PhD, and for students in the second year of their PhD who have begun to nail down a thesis topic. This course will involve a combination of: (1) student-led discussions; (2) lectures designed to cover general and sometimes controversial scientific issues; and (3) short presentations by students introducing the background and context for their proposed research. The major assignment for the course is an essay that requires students to discuss their thesis research within the broader context (historical and contemporary) of their general research field.

The class will read about and discuss topics that include:

• best practices and controversies about approaches to science and research tactics
• common errors in experimental design and statistical analysis (note: some background in statistics is useful, but not required)
• brief overviews of some novel statistical approaches
• ethics, science communication, publishing, and other issues that are important to researchers

Location: St. George, room: ESC 2144
Date & Time: Thursday: 11:30am-2:45pm (includes a 20 min break, start date: Sept. 12)

Instructor(s): M. Krkosek

Course Description: The Core Ecology course will provide students with a foundation of the conceptual basis of ecology through lectures, readings, and discussions. The structure of course content will follow the four levels of ecological organization: (1) individuals, (2) populations, (3) communities, and (4) ecosystems. By exploring the theoretical foundations of ecology and the linkages among ecological theories, students will gain a broad perspective of the historical development and current trends in ecology, particularly at the population and community levels. This course should be useful preparation for PhD students for the Question Bank part of the Appraisal Exam. This course should be useful preparation for PhD students for the Question Bank part of the Appraisal Exam. 

If you have taken two or more 3rd or 4th year undergrad courses in ecology, the course may not be especially useful for providing you with background knowledge—discuss with your supervisor and supervisory committee whether you should take this course. On the other hand, if you have no or very little background in the field, you may need to sit in on undergrad lectures or do outside reading before you take this course; however, if you have taken other courses in evolution, math, or biological theory, ask the EEB graduate office and your supervisor and committee whether you will be equipped to take this course.

Location: Hybrid
Date & Time: TBA

Instructors: A. Agrawal, S. Wright

Course Description:  The Core Evolution Course will cover the basics of evolution through a combination of lecture, paper discussion and student presentation. We will cover four major topics in the field, a) population genetics, b) the evolution of sex and mating systems, c) speciation, and d) evolutionary conflict and cooperation. This course should be useful preparation for PhD students for the Question Bank part of the Appraisal Exam. If you have taken two or more 3rd or 4th year undergrad courses in evolution, the course may not be especially useful for providing you with background knowledge—discuss with your supervisor and supervisory committee whether you should take this course. On the other hand, if you have no or very little background in the field, you may need to sit in on undergrad lectures or do outside reading before you take this course; however, if you have taken other courses in ecology, math, or biological theory, ask the EEB graduate office and your supervisor and committee whether you will be equipped to take one of these courses.

Location: St. George
Date & Time: Monday: 2:00-5:00pm, ESC2142 (start date: Jan 8, 2024)

Instructor(s): M. Johnson

Course Description:  Goals: The goals of this graduate course are to provide students with a broad introduction to classic ideas, concepts and theories in ecology and evolution, as well as the most recent cutting-edge developments across these fields. This will be accomplished by having students read one classic and one cutting-edge paper on each of 12 topics, equally split across the breadth of ecology and evolution. Students will develop oral skills by presenting two papers in the course and by leading discussions. Grantspersonship will be promoted through the writing of one short grant structured around addressing classic questions with cutting-edge techniques. Writing and critical thinking skills will be developed through a synthesis paper written in the style of an Opinion article at Trends in Ecology and Evolution     

Learning outcomes: By the end of the course students are expected to obtain the following learning outcomes:
1) Gain a broad understanding of classic questions in ecology and evolution as well as the state-of-the-art techniques, ideas and knowledge in the field.
2) Learning grant writing skills.
3) Developing oral skills in relation to the presentation and discussion of scientific ideas.
4) An ability to synthesize classic concepts in the field and articulate existing gaps in knowledge and future directions for research.

Location: UTM
Date & Time: Wednesday: 12:30-3:30pm

Instructor(s): M.J. Fortin

Course Description: Biologists need to use statistical methods to test their hypotheses. Given the increasing complexity of experiments carried out by biologists, they need however to understand the limitations of these statistics, how to select the appropriate statistics for their needs, and how to interpret them properly—both statistically and biologically. The goal of this advanced course in statistics is to teach biologists how to choose and use statistics, so that they can address relevant biological questions and test them with the appropriate methods. Specifically, an overview of advanced notions about regression analysis and ANOVA will be presented. The course is lecture-based with assignments designed to develop awareness about the misuse of statistics.

Location: St. George, ESC 2144, Course Syllabus
Date & Time: Tuesday: 10:00am-12:00pm (Sept 10 – Dec 3)

Instructor(s): TBA
Course Description: TBA
Location: TBA

Instructor(s): M. J. Fortin

Course Description: Ecological processes are inherently spatially structured due to spatial dependence on environmental conditions and spatial autocorrelation of species behaviours. The goal of this course is to provide a broad overview of the various spatial analytical methods available to quantify (geostatistics, network theory, boundary detection), test (restricted randomization) and model (spatial regressions) spatially autocorrelated ecological data. Students will be introduced to concepts of spatial scales and how multiscale analysis can be performed with census and sampled data. Furthermore, specific spatial methods to deal with point pattern data and surface pattern data will be reviewed. A combination of lectures and computer laboratory sessions will be used.

Location: St. George, ESC 2144, Course Syllabus
Date & Time: Thursday: 10:00am-12:00pm (Sept. 12- Dec 5)

Instructor(s): H. Rodd

Course Description: A short graduate course module focused on developing the academic and professional skills required to succeed during and beyond graduate education in life sciences, with an emphasis on ecology and evolution. Employers from government, NGOs, public school systems, private sectors, etc. emphasize that they look for employee skill sets that include critical thinking, problem-solving, communication, creativity, teamwork, interpersonal relationships, administration, and leadership. This course will provide graduate students with:

1) opportunities to think, learn and communicate in a broad integrative manner and experience working in small interdisciplinary groups;

2) access to material for networking and non-laboratory skill assessment tools needed for success in academic and non-academic life science careers; and

3) opportunities to strengthen communication skills, including face-to-face networking with professionals who have careers outside the academic environment (e.g., government scientists, environmental consultants, NGOs, biotech companies).

Topics for interactive lectures by faculty and invited lecturers, and classroom discussions, may include: the practical aspects of succeeding in graduate school; enhancing research skills; problem-solving techniques; leadership; finding successful collaborations; developing strong written and oral communication skills; opportunities to enhance imaginative, creative, and innovative thinking; further training as a postdoctoral fellow; effective networking; integrating family commitments; career transitions; CVs and resumés; career options in and out of academia; best methods of searching for and landing the job; staff management; global scientific issues; social implications; and maintaining career development.

Location: Hybrid
Date & Time: TBA

Instructor(s): R. Ness

Course Description: This course will introduce students with typical EEB backgrounds to concepts and practices in computational analysis with a special focus on DNA/RNA/Protein sequence data and genomics. The course consists of 12 interactive labs that combine text instructions, with live coding examples and challenging exercises. Students will be guided through a thorough introduction to the programming language Python, with emphasis on techniques and concepts most important to biological research. The last four labs also include a brief introduction to the bash command line environment as well as numerous command line bioinformatic packages. These packages are core tools in modern analysis of high throughput sequence data. Examples include, Trimmomatic, samtools, BWA, GATK HaplotypeCaller, SPADES de novo, bowtie, cufflinks etc. No prior knowledge of programming is required but students should have a firm grasp of basic genetic concepts such as DNA replication, inheritance and protein synthesis. Each of the 12 labs contains 5-10 questions which will be submitted and graded weekly.  The course is largely self-directed via working through the lab. The course will meet weekly to facilitate peer-to-peer collaborative problem solving and so students can receive guidance from the instructor or TA.

Location: St George

Instructor(s): H.Wagner, M.J. Fortin

Course Description:
Interdisciplinary overview of the field of landscape genetics, catering to students in basic and applied ecology, conservation and population genetics, landscape ecology, evolutionary biology and conservation biology. A key objective of landscape genetics is to study how landscape modification and habitat fragmentation affect organism dispersal and gene flow across the landscape. Landscape genetics requires highly interdisciplinary specialized skills, making intensive use of technical population genetic skills and spatial analysis tools (spatial statistics, GIS tools and remote sensing). Students can choose from three credit options (conceptual exercises, computer labs with R, or group project). The optional R labs are designed to help participants develop their R programming skills. The course is part of an international distributed graduate course.

Location: St. George
Date & Time: Wednesday: 10:50am-2:00pm (Jan 10-May 1)

Instructor(s): J. Sztepanacz

Course Description: Evolvability is a fundamental concept in evolutionary biology. Despite the importance of evolvability in evolutionary theory, a focus on the study of evolvability itself, is relatively recent. The flood of recent research has largely been pursued independently by different fields, leading to different interpretations and approaches for the study of evolvability.

In this course we will be working through select chapters of the book “Evolvability” edited by Thomas F. Hansen, David Houle, Mihaela Pavlicev, and Christophe Pelabon. The book focuses on the study of evolvability from a variety of perspectives: from theory-of-science, to evo-devo and systems biology, to evolutionary quantitative genetics, and macroevolution. The authors of the book review what has been learned during the past 25 years of research on evolvability and answer key questions that allow us to understand evolvability as one of the unifying concepts of the extended synthesis of evolutionary biology. We will begin the course with an introduction to quantitative genetics and how it is used to study evolvability. This will provide the foundation for the book sections focusing on evolutionary genetics and macroevolution, which we will focus on in this course.

Location: St. George, room: ESC 2142
Date & Time: Tuesday: 10:00am-1:00pm (Oct. 22 – Dec. 3)

Instructor(s): R. Murray

Course Description: Our goal is to provide students with a broad introduction to classic ideas, concepts, and theories in evolution. We will accomplish this goal by having students read classic papers on various topics, from across the breadth of evolution. These readings will provide students with a solid foundation in the field that enables them to identify the central questions that evolutionary biologists are striving to resolve.

Location: Mississauga, room: MN6128 (note – first class in room CC 4034), Course Syllabus
Date & Time: Alternating Tuesday: 10:00am-1:00pm (Sept. 17 – Dec. 3)

Instructor(s): R. Reisz

Course Description:
Paleontology is the study of fossils, and these include body fossils, impressions, body residues, trackways. The goal of this course is to provide a broad overview of the history of paleontology, current topics of interest to graduate students, including why fossils are important in Ecology and Evolution, approaches to increase the impact of research, modern visualization and analytical methods for the study of fossils. Students will be introduced to fossil tissue histology, handling of CT data, and how to combine these two major sources of information at the macro and micro levels. Furthermore, specific papers of interest will be evaluated in a group setting. A combination of lectures and potential laboratory sessions will be used.

Location: Mississauga, DV 3016, Course Syllabus
Date & Time: Friday: 10am – 1pm (Jan 10-Feb 28)

Instructor(s): C. Baines, N. Mideo

Course Description: 6-7 week module, in the first half of the Summer term; course weight: 0.25.

In this course we will discuss the historical legacy of bias and exclusion in science, recent data that quantifies the scale of the problems and/or the science behind them, and finally evidence-based strategies to overcome these issues. This course is inspired by, and modified from, a course designed by Prof. Corrie Moreau at Cornell (read about it here).

Location: St. George
Date & Time: Monday: 10:00am-12:00pm room ESC1014 (starting Oct 16)

Applied Aquatic Ecology

A seminar-style course with student-led discussions. Introductory focus on basic aquatic ecology, followed by deep dives into how scientific studies have challenged and/or enhanced our understanding of how climate change, fisheries, and pollutants have impacted aquatic ecosystems.

Evo-Eco Module V: Phylogenetic Comparative Methods rapid adaptation

Instructor: L. Mahler
Course Description: 6-week module, in the second half of the Winter term; there will be a 3-hour session each week; course weight: 0.25. Draft syllabus.

Integrative Biology of Behaviour

Course focuses on behaviour genetics, genomics and neurobiology, taught by faculty from EEB and Cell & Systems Biology.

Introduction to Bayesian Analysis in Ecology & Evolution

Instructors: M. Fortin and M. Farrell
Bayesian statistics are more and more used to fit statistical models in ecology and evolution. In this course, the graduate students will be working through the book Statistical Rethinking 2nd Edition by Richard McElreath. The book concentrates on the explanation and applications of Bayesian statistics using practical, hands-on code demonstrations and exercises in the R programming language and/or STAN. Students will be required to attend weekly meetings where we will collaboratively cover select chapters (mostly those presenting how to perform regressions—mixed models, etc.). Each meeting will be lead and presented by one or more students presenting the major concepts and challenges arising from the R exercises.

Introduction to Statistical Learning

Statistical learning is set of tools for exploring complex datasets. The analysis of big data blends developments in statistics and computer science using methods such as the lasso and sparse regression, classification and regression trees, and boosting and support vector machines. The course concentrates on the explanation and applications of statistical learning using practical, hands-on code demonstrations and exercises in the R programming language.

Population genomics of rapid adaptation

Instructor: S. Wright
There is growing recognition of the widespread importance of rapid adaptation for both ecological and evolutionary processes. Rapid adaptation can have major impacts on the ecology and evolution of species, and it also lies at the intersection of basic and applied questions in EEB (e.g. evolution of drug and herbicide  resistance, adaptation to pollution and climate change). The ability to sequence large numbers of genomes and track evolutionary changes in real time is allowing for exciting opportunities to gain major new insights into rapid adaptation. This course will take the form of a working group, where we will discuss, explore and synthesize the progress, prospects and pitfalls of utilizing evolutionary genomic approaches to better understand the causes and consequences of rapid adaptation. We will draw from everyone’s expertise and interest to develop a synthesis and roadmap for the field. 

Special Topics in Evolution: Genomics

Instructor: R. Ness
Course Description: The genome has been referred to as the blueprint of life and consists of the full complement of genes and genetic material carried by an organism. The ongoing revolution in DNA sequencing allows biologists to observe the variety of genetic and genomic structures that underpin the diversity of life. In addition, applications of genomic technologies have facilitated new fields of research such as personalized medicine and evolutionary genomics. The lectures will focus on the diversity of genomic structures, their functions and evolutionary origins. The course also has computer-based practicals that provide hands-on training with cutting-edge bioinformatic tools for analysis of genome-scale datasets and next generation sequencing data. A working knowledge of Python is a pre-requisite.

Instructor(s): P. Molnar

Course Description: Modelling is a critical tool for describing the complex dynamics of ecosystems and for addressing urgent management questions in ecology, epidemiology and conservation. In this practical introduction, students learn how to formulate ecological and epidemiological models, link them to data, and implement/analyze them using computer simulations. The course includes approaches for modelling individuals, populations, and communities, with applications in population viability assessments, natural resource management and food security, invasive species and pest control, disease eradication, and climate change mitigation. While not a prerequisite, some experience with computer programming will be beneficial for this course.

Location: Scarborough
Date & Time: Tuesday (lecture): 12:00-2:00pm
Friday (computer lab): 1:00-3:00pm (Sept 3 – Dec 3)

Instructor(s): M. Frederickson and M. Freedman

Course Description: Major concepts in ecology and evolution from the perspective of plant-animal interactions. The richness of interactions between plants and animals is explored including antagonistic interactions (e.g., herbivory, carnivorous plants), mutualistic interactions (e.g. pollination, seed dispersal, ant-plant associations), and interactions involving multiple species across trophic levels.

Location: St. George
Date & Time: Tues & Thurs 1:00-2:00pm (Sept 3 – Dec 3)

Instructor(s): C. Rochman

Course Description: The ocean covers more than 70% of the surface of our planet, and is critical to all life on Earth by producing oxygen and regulating the climate. The main focus of this course is on the ecology of our oceans. Through lectures and hands-on activities, this course will cover geomorphology, the marine abiotic environment, marine biodiversity, and marine biogeochemistry – each of which are features that interact to influence our ocean ecosystems. Students will also get a flavour of what it means to be a marine ecologist through the lens of different subtopics and career options.

Location: St. George
Date & Time: Tues: 10:00am-12:00pm, Lab: Thur. 1:00-4:00pm

Instructor(s): M. Osmond

Course Description: The study of ecology and evolution uses models to explain biological phenomena including the maintenance of biodiversity, population growth, competition, eco-evolutionary dynamics, trait and molecular evolution, epidemiology, spatial ecology, phylogeny and extinction. Students will learn to develop, assess and apply analytical, simulation and statistical models for analysis and data interpretation. Note that an undergrad course in calculus and an undergrad course in ecology or evolution are recommended.

Location: St. George
Date & Time: Mon & Wed 10:00-11:00am

Instructor: T. Parins-Fukuchi

Course Description: Probability and stochasticity underlie our understanding of evolutionary and ecological processes. Famous models such as Hubbell’s “grand unified” theory, Kimura’s neutral theory of molecular evolution, and the Wright-Fisher and Moran models in population genetics are all stochastic processes. Stochasticity also underlies statistical inference, from simple approaches like linear regression to complex models in coalescent theory and phylogenetics. In this course, we will explore and seek to better understand these stochastic models by building them from the ground up using Python. We will not focus on mathematical proofs in this course, but instead work with these models using simulation. In doing so, we will sharpen our statistical approaches for research and develop our intuition for the fundamentals of modelling to improve our ability to form and grasp “big picture” biological hypotheses. Lastly, students will gain a working knowledge of the Python programming language in taking this course. 

Very little previous programming experience is required. If you are interested in taking the course and are nervous about this aspect, please reach out to me and we will work it out. What ever you know will almost certainly be enough for us to get going. Our goal will be to develop these skills as we go. 

Example topics: probability, likelihood, and Bayes; Markov models; diffusion processes; phylogenetics.

Location: St. George, Building: TBA
Date & Time: Thursday from 3:00pm-6:00pm

Instructor(s): S. Stefanovic

Course Description: Lectures will provide an in-depth coverage of modern methods of phylogenetic reconstruction including molecular systematics based on DNA sequences. The principles and philosophy of classification will be taught with an emphasis on ’tree-thinking’, one of the most important conceptual advances in evolutionary biology. Tutorials will focus on recent developments in the study of evolutionary patterns while gaining proficiency in reading, presenting, and critiquing scientific papers. Shuttle Bus (Hart House to/from UTM – if running) stop is right in front of IB.

Location: Mississauga, Building: IB-385
Date & Time: Tues & Thur from 3:00pm-5:00pm (Sept 3- Dec 3)

Instructor(s): J. Sztepanacz

Course Description: Quantitative genetics is the study of complex traits that are affected by many genes and where non-genetic factors may also be important. Its basis is in statistical models and methodology and when applied to humans is also known as statistical genetics. The study of quantitative genetics is fundamental to understanding evolutionary dynamics of populations, complex diseases, variation and covariation among relatives, and for agricultural improvement through selective breeding. Through lectures and hands-on computer labs students will learn the fundamental concepts of quantitative genetics, how to analyse quantitative genetic data, and how these approaches can help us answer key questions in evolution.

Location: St. George
Date & Time: Thursdays 10am-12pm and Friday 12:00-1:00pm

Instructor(s): A. Agrawal

Course Description: A focus on theoretical population genetics, using mathematical models to understand how different evolutionary forces drive allele frequency change. Students learn how to mathematically derive classic results in population genetics. Topics include drift, coalescence, the relationship between population and quantitative genetics, selection in finite populations, and mutation load. Offered in alternate years. Recommended Preparation: an intro course to genetics and a solid understanding of basic algebra and calculus.

Location: St. George, ESC 3087
Date & Time: Mon: 10:00am-12:00pm (Jan 6-Apr 4, 2025)

Instructor(s): B. Chang

Course Description: Processes of evolution at the molecular level, and the analysis of molecular data. Gene structure, neutrality, nucleotide sequence evolution, sequence evolution, sequence alignment, phylogeny construction, gene families, transposition.

Location: St. George
Date & Time: Wed 10:00-11:00am, Friday 10:00-12:00pm (Sep 3-Dec 3)

Instructor(s): S. Claramunt Tammaro, D. de Carle

Course Description: 

Phylogenetic trees are now fundamental in many areas of biology. This course provides as in-depth introduction to phylogenetic biology including theoretical foundations, approaches to phylogenetic tree inference, and applications of phylogenetic trees in ecology and evolution. Students will gain skills in bioinformatics, with application to DNA sequence analysis and phylogenetic tree inference. Through a combination of lectures, discussions, and computer labs, students will master the theory and practice of phylogenetic tree inference and phylogenetic methods in ecology and evolution.

Credit Value (FCE) 0.50

Location: St. George
Date & Time: Tuesdays: 3:00 – 5:00 pm

Course Description: After providing students the basics in STA 2080-Fundamentals of Statistical Genetics, this research oriented course will introduce advanced topics to students who are interested in pursuing a career in genome data science.  The specific topics will evolve, over the years, depending on the latest analytic needs and scientific developments from the genetic community. Topics might include set-based statistical analyses for joint analyzing multiple genetic factors (i.e. gene-based), multiple genes(i.e. pathway), multiple outcomes (i.e. pleiotropy), multiple studies (i.e. meta),data-integration analyses for integrating all kinds of ‘omic’ data, and selective inference for conducting reproducible research.

Course Description: Here is a list of topics/exercises that are covered most in depth to help students decide if it is right for them: 

• Searching for and down loading data from a bunch of genome, protein and molecular databases; 
• Analyzing molecular and genomic data in R; 
• Generating protein models from molecular data;  
• Protein function analyses (as you can tell there is a lot of bioinformatics as it relates to proteins); 
• a couple of lectures on phylogenetics, but this topic is not covered extensively

Other bioinformatics courses offered at the University of Toronto: JTB 2020H, CSB4271H, CSB474H1S, MSB 4174

Visit theCSB webpage for course offerings. 

Note: CSB students may be given priority for signing up for these courses.

This is a beginner’s introduction to R and R-Studio for individuals with no prior experience or background. Individuals who complete the course will be able to: work in the R-Studio environment; understand data structures and data types; import data into R and manipulate data frames; transform ‘messy’ datasets into ‘tidy’ datasets; make exploratory plots as well as publication-quality graphics; use flow control; use string manipulation to clean data; and perform basic statistical tests and run a regression model. Each class will consist of a short introductory lecture followed by ‘code-along’ hands-on learning. Students are expected to have access to a computer during class. The course will be provided through Quercus using Bb-collaborate.

This course is designed to serve as an introduction to genomic data science for students who do not have a background in bioinformatics. Students in the course will learn to perform basic genomic data analyses using both Galaxy (a web-based platform that incorporates multiple bioinformatics tools into an easy to use GUI) and the Unix environment. During the course, students will learn how to: use Galaxy and command line tools to process and manipulate data; use of the Integrative Genomics Viewer to visualize genomes; work in a Unix terminal; install bioinformatics software; connect and work on remote servers; understand common genomics file formats; and perform de novo genome assemblies, reference-based genome assemblies, genome annotation, variant calling, and RNA-seq data analysis. Each class will consist of a short introductory lecture followed by ‘code-along’ hands-on learning. Students are expected to have access to a computer during class. The course will be provided through Quercus using Bb-collaborate.

This is a beginner’s introduction to Python for data science applications. The course is intended for students with no computer science background who want to develop the skills needed to analyze their own data. Students who complete this course will be able to: perform data analysis in Python using the Jupyter Lab environment; understand data structures and data types; import data into Python and manipulate Python objects such as list, data frames, and dictionaries; transform ‘messy’ datasets into ‘tidy’ datasets; make exploratory plots; and use flow control and use string manipulation to clean data. The structure of the class is ‘code-along’ and students are expected to have access to a computer during class. The course will be provided through Quercus using Bb-collaborate. 

Instructor(s): M. Escobar (Public Health)

Instructor(s): D. Fishman (Public Health)
Location: Online

Course Description: This applied microbiology course introduces students to microbial activities with environmental implications in diverse areas such as public health, bioremediation, agriculture and green technologies. A key focus of the course is to introduce classical and advanced molecular methods used to detect and quantify microbes, and microbial activities, in environmental samples. Students are given the opportunity to perform microbial enumeration and characterization techniques in the lab to supplement the lectures. Timetable information and syllabus.

Instructor(s): Terrence H. Bell
Location: Scarborough

Course Description: This course provides an introduction to the rapidly growing field of ecological and environmental modelling. Students will become familiar with most of the basic equations used to represent ecological processes. Registration and details can be found here.

Location: Scarborough

Course Description: In this course data analysis techniques utilizing Python and R statistical language will be discussed and introduced, as well as the basics of programming and scientific computing. Registration and details can be found here.

Location: Scarborough

Registration and course description
Location: Scarborough

Instructor(s): TBA

Course Description: Canada has a complex conservation landscape. Through lectures and interactive discussions with leading Canadian conservation practitioners, this course will examine how conservation theory is put into practice in Canada from our international obligations to federal and provincial legislation and policies, and the role of environmental non-government organizations.

Location: Online – synchronous
Date & Time: Monday 2:00pm-5:00pm

Instructor(s): TBA

Course Description: The field of ecology is rapidly changing and this course will cover recent advances, concepts or controversies in ecology. This course will focus on specific scientific issues using current literature and the learning experience will be augmented by student presentations and discussions. The course will help ensure that students become familiar with current understanding and basic ecological concepts.  This will be an elective course, and will be especially attractive to those students who did not take advanced ecology courses during their undergraduate studies.

Location: Scarborough (in person)
Date & Time: Wed 5:00pm-7:00pm

This course will cover Bayesian statistics, as well as linear models, GLMs, penalized regression, random effects, abundance estimation, habitat selection/step selection functions, point processes, occupancy modeling.

Location: ESC 1042
Date & Time: Tuesdays 10am – 12pm. 

The Faculty of Information offers graduate courses in topics including: Introduction to Statistics for Data Science, Data Analytics: Introduction, Methods, Practical Approaches. View courses here.

This course is offered in conjunction with GGR414H Advanced Remote Sensing. Building on GGR337H1 Environmental Remote Sensing (also offered as a graduate course GGR1911H), which covers the basic theories and techniques of optical and microwave remote sensing of the land surface, GGR1916H introduces advanced theories and techniques for land cover mapping, retrieval of vegetation structural and physiological traits, and remote sensing of vegetation light use efficiency and photosynthetic capacity. Diagnostic ecosystem models will also be introduced for terrestrial water and carbon cycle estimation using remote sensing data. Optical instruments for measuring vegetation structural parameters in the field will be demonstrated, and high-resolution remote sensing images acquired from a drone system will be used as part of the teaching material and lab assignments. For GGR1916H additional lectures will be offered on basic radiative transfer theories as applied to remote sensing of vegetation traits and function. Exclusion: GGR414H.

Instructor(s): A. Stinchcombe

Course Description: An undergrad/grad joint course. View draft syllabus. The prerequisites are MAT223H1and MAT244H1 with a recommendation of a probability course.

Course Description: Students must take 2 course topics by the end of their second year in order to complete this course. The mark in this course is the average of the two marks obtained in the topics taken. Topics include: A Practical Course in Programming for Biologists; Background and Topics in Molecular Genetics, Functional Genomics, and Computational Biology. Learn more

This course was sponsored by the Institute of Medical Sciences (IMS) in previous years, and if offered this year may have a few spots open for students “external” to IMS. These spots are very limited, and therefore anyone interested in this course should register via ACORN as soon as possible.

Course Description: This course focuses on the collective behavior of cellular populations coordinated and regulated by intra- and inter-cellular genetic and signaling pathways. We will introduce the mathematical tools to model such non-linear processes, both in a deterministic as well as stochastic framework. Topics cover biological case studies such as microbial population dynamics, the mammalian immune response, disease epidemics, cell differentiation, development and morphogenesis, and the behavior of neuronal assemblies

Instructor(s): M. Holmes

Course Description: This course will focus on the development and adult organization of neurobiological mechanisms underlying the perception of social information and production of social behaviours in diverse species. Each week will focus on a unique topic (e.g., eusociality in hymenoptera; pair bonding in voles; face perception in humans; etc)incorporating a mix of lecture, primary literature, and group discussion. 

Note: for courses given directly by Scinet without a course code:
1. Register with Scinet (H. Rodd can be entered as your sponsor if your supervisor doesn’t have an account)
2. Sign up for Scinet courses using EEB course codes (provided by H. Rodd). 

Please check courses.scinet.utoronto.ca for updates. 

Course Description: Statistical analysis of genetic data is an important emerging research area with direct impact on population health. This course provides an introduction to the concepts and fundamentals of statistical genetics, including current research directions. The course includes lectures and hands-on experience with R programming and state-of-the-art statistical genetics software packages.

Course Description: This course provides an introduction to a scholarly approach to teaching statistics in higher education. Emphasis is placed on the use of statistics education research, effective communication of fundamental statistical concepts typically encountered in introductory statistics, alignment of learning outcomes, course activities and assessments, recognition of common misconceptions and how to address them, and effective integration of educational and statistical technologies. No prior teaching experience is necessary.   

Course Description: This course will cover the main approaches to developing a theory of statistical inference. A central theme of the course is a discussion of the reasons why no particular theory has obtained anything near universal acceptance and what the implications of this are for the subject of statistics. Pure likelihood theory, optimality-based frequentitist and Bayesian theories, fiducial inference as well as more qualitative approaches, such as the usage of p-values, are all considered. The desiderata for an ideal theory are discussed and whether or not it is possible for a theory satisfying all such criteria can be obtained. Evaluation will be based on assignments and a project. The overall aim of the course is to inculcate a critical attitude towards the consumption and development of statistical methodology. 

Course Description: A central issue in many current big-data scientific studies is how to assess statistical significance while taking into account the inherent large-scale multiple hypothesis testing. This 6-week graduate course will first go over the fundamental elements of single and multiple hypothesis testing, then it will move on to more advanced topics such as incorporating prior information to improve power, specific applications to whole genome genetic association studies, as well as discussions of the fallacy of p-value and alternative measures of statistical evidence and significance. Both analytical and empirical arguments will be presented, and participating students are expected to write a research report on suggested or self-selected topics related to multiple hypothesis testing.

Course Description: A Google search with the terms “Markov chain Monte Carlo (MCMC)” returns over 2 million hits. This is not surprising, as this class of algorithms has become in the last 30 years the main workhorse for statistical computation, especially for Bayesian inference. However, the evolution of scientific experiments, particularly the availability of large data and the complexity of posited models have brought MCMC to an inflection point. Significant difficulties are encountered when the data is massive or when the statistical model is complex enough to be analytically intractable. In the former case, the classical MCMC samplers scale poorly while in the latter only approximate versions of the model can be studied with little, or no theoretical guarantees of accuracy. In this course we will discuss and study computational algorithms that overcome this type of challenges.

Course Description: This quarter-credit (0.25 FCE) course provides an overview of the core areas of demography (fertility, mortality and migration) and the techniques to model such processes. The course will cover life table analysis, measures of fertility and nuptiality, mortality and migration models, and statistical methods commonly used in demography, such as Poisson regression, survival analysis, and Bayesian hierarchical models. The goal of the course is to equip students with a range of demographic techniques to use in their own research.

Instructor(s): TBA

Course Description: A graduate-level/postdoc course in which students read recent literature on pedagogical theory, and participate in exercises and group discussions on how to apply that theory to the university classroom. TBA. May be offered Winter 2020. See website for additional information. This course is also available to postdocs.

Note: This course is for professional skills development—you will not receive course credit for this course

With the professor’s permission, students are welcome to sit in on undergraduate courses to enhance their background in specialized topics.

Students may also continue to audit, or take for credit, graduate courses even after they have completed the course requirements for their degree.

If you identify a need for a course on a particular subject that is not currently covered by available courses, please notify the graduate department and/or faculty. If student demand is high enough, it may be offered as a new Special Topics course the following year.

The Living Data Project offers these four one-month courses.

1. Productivity and Reproducibility in Ecology & Evolution 
2. Scientific data management for ecology and evolution
3. Synthesis Statistics for Ecology and Evolution 
4. Scientific collaboration in Ecology, Evolution and Environmental Science 

Prospective student instructors or organizing groups should submit to the Graduate Office a proposal that: 

1. describes the need and the project designed to fill it, including giving it a descriptive name
2. assesses the demand within the department and specifies a measure of success for the project (e.g. the number of students completing the training)
3. optional: describes a small budget (maximum $500) for supplies, course resources, and/or a small honorarium for the chief instructor;
4. provides a list of fewer than 10 questions that would constitute a suitable evaluation of the quality or success of the project in meeting student needs.

The Grad Office will respond with a decision about the level of support, which may be contingent on the project meeting its specified index of success. If the project is a success, we will also provide a letter of documentation to students who led the project.