**BIO2042 - Biostatistics II**

Introduction to *general* and *generalized* linear models in ecology.

**--------------------------**

**BIO3811 - Ecological Modelling**

This course will introduce students to the concepts and techniques used in ecological modelling as well as their applications to natural resource management. Weekly laboratory assignments will help students develop technical skills in programming and conceptual skills in simulation modelling. The course will be divided into two parts. In the first part, students will be introduced to the philosophy of modelling in the biological sciences as well as basic models of population growth, species interactions, and metapopulation dynamics. The second section will present spatially explicit models used to examine ecosystem dynamics, including forests disturbance models (e.g., fire, insects, logging), forest succession, and models of spatial connectivity. Working together in small teams, students will develop their own models to address specific spatial ecological questions. A final project will require students to demonstrate their capacity to conceptualize, construct, and validate a model and then summarize and interpret the results. Throughout the course, the importance of creativity in solving ecological modelling problems is emphasized.

**BIO6720 - Graduate seminar in Landscape Ecology**

Landscape ecology is a field that provides a generally large scale and spatial framework for the investigation of theoretical and applied ecological questions as well as for natural resource management. This course will introduce students to landscape ecology through readings of the primary literature, seminar presentation, group discussion, and hands-on laboratory exercises to develop experience and skills with the quantitative tools used in landscape ecology. Topics to be covered include spatial scale, forest disturbances such as fire and insects, spatial statistics and null models, simulation modelling, habitat connectivity/fragmentation, and applied landscape management.

**Landscape Genetics (Given during the 2015 MFC Summer School)**

The objective of this short course is to introduce students to the rapidly developing field of landscape genetics. Landscape genetics is a synthetic discipline that requires background in multiple disciplines including population genetics, landscape ecology, and spatial statistics. We will briefly cover each of these topics and address what sort of questions we can address using a landscape-genetics approach.

**Introduction to Landscape Modelling - given during the 2012 Modelling Forest Complexity Summer School**

In this three day intensive course students are introduced to the theory and methods of landscape modelling with particular emphasis on modelling forest disturbance dynamics. The course will include discussion of the philosophy of modelling, the motivation for landscape scale studies, and how modelling-based approaches can help us to better understand and characterise complex forest system dynamics. Hands on exercises will be used to introduce the concepts and essential elements of landscape modelling including Markov-chain succession models and cellular automata using R. Students will also receive an introduction to SELES, the “Spatially Explicit Landscape Event Simulator,” a highly flexible and powerful tool for modelling landscape dynamics. By the end of course students will understand basic concepts in landscape modelling and be able to use and develop simple models using the SELES platform and analyse their output. We will also have the opportunity to develop a landscape model based on a question developed in class.

BIO2041 – Introduction to BiostatisticsThe goal of this course is to provide a basic understanding of statistical analysis in the biological sciences. At the end of the course, the student will be able to: (1) present and summarize data; (2) select the appropriate method of analysis given a statistical problem and data set; (3) understand fundamental statistical theory; (4) undertake basic calculations using the scientific programming language R; and (4) correctly interpret the results from statistical tests. Throughout the course, emphasis is placed on student independence and problem–solving skills.

Syllabus 2015--------------------------