Course Overview: Spatial modelling is increasingly being used in ecology and evolutionary biology for both basic and applied research questions. While emphasis traditionally has been on species-level niche modelling, the increasing availability of genomic and community-level data has increased interest in modelling biodiversity patterns above and below the species level. This 5-day course will provide a thorough introduction to different spatial modelling techniques for quantifying and visualizing patterns of biodiversity across scales of biological organization - from population-level genetic variation,…

Course Overview: The course will describe recent methods (concepts and R tools) that can be used to analyse spatial patterns in community ecology. The umbrella concept of the course is beta diversity, which is the spatial variation of communities. These methods are applicable to all types of communities (bacteria, plants, animals) sampled along transects, regular grids or irregularly distributed sites. The new methods, collectively referred to as spatial eigen-function analysis, are grounded into techniques commonly used by community ecologists, which…

Course Overview: The course will deal with different aspects related to the use of remote sensing and GIS in spatial ecology by using the Free and Open Source Software GRASS GIS coupled with R. By the end of this 5-day practical course, attendees will have the capacity to deal with ecological patterns and processes by using GIS and remote sensing algorithms. The increasing availability of open ecological and geographical data through networks such as the Global Biodiversity Information Facility (GBIF,…

Course Overview: Python is a dynamic, readable language that is a popular platform for all types of bioinformatics work, from simple one-off scripts to large, complex software projects. It gives an overview of the language with an emphasis on practical problem-solving, using examples and exercises drawn from various aspects of bioinformatics work. After completing the workshop, students should be in a position to (1) apply the skills they have learned to tackle problems in their own research and (2) continue…

Course overview: Python is a dynamic, readable language that is a popular platform for all types of bioinformatics work, from simple one-off scripts to large, complex software projects. This workshop is aimed at people who already have a basic knowledge of Python and are interested in using the language to tackle larger problems. In it, we will look in detail at the parts of the language which are particularly useful in scientific programming, and at the tools Python offers for…

This course is being provided through a collaboration of PR statistics and Proteus and will be delivered by Dr. Darryl MacKenzie Course Overview: The presence or absence of a species across a set of landscape units is a fundamental concept widely used in ecology (e.g., species range or distribution, epidemiology, habitat modeling, resource selection probability functions, as a monitoring metric, metapopulation studies, biodiversity and species co-occurrence). An important sampling issue, however, is that a species may not always be detected…

Course Overview: This workshop will provide students with the opportunity to learn how to use social network analysis to analyze social relational datasets such as human friendship networks or animal social networks. Attendees will learn how to use R and several R packages including igraph, sna, network, asnipe, timeordered, tsna to create network graphs, calculate descriptive network metrics, use randomization and random models to evaluate the significance of these metrics, determine graph structural properties including community structures, use QAP and…

Course Overview: This course will provide an introduction to modern multivariate techniques, with a special focus on the analysis of abundance or presence/absence data. Multivariate analysis in ecology has been changing rapidly in recent years, with a focus now on formulating a statistical model to capture key properties of the observed data, rather than transformation of data using a dissimilarity-based framework. In recent years, model-based techniques have been developed for hypothesis testing, identifying indicator species, ordination, clustering, predictive modelling, and…

Course Overview: Precision medicine refers to the use of prevention and treatment strategies that are tailored to the unique features of each individual and their disease. Analysis of high throughput genome and transcriptome data is major component of new large-scale precision medicine efforts. This analysis involves the identification of specific genome or transcriptome features that predispose an individual to disease, predict response to specific therapy, or influence diagnosis and prognosis. During this course (PMBI01), students will perform an end-to-end precision…

Course Overview: Metabarcoding techniques are a set of novel genetic tools for assessing biodiversity of natural communities. Their potential applications include (but are not limited to) accurate water quality, soil diversity assessment, trophic analyses of digestive contents, early detection of non-indigenous species, studies of global ecological patterns and biomonitoring of anthropogenic impacts. This course will give an overview of metabarcoding procedures with an emphasis on practical problem-solving and hands-on work using analysis pipelines on real datasets. After completing the course,…

Course Overview: This 5-day course will cover the concepts, methods, and R tools that can be used to analyse spatial data in ecology. The start of the course will cover the basics of linear models and spatial data processing in R and provide a common ground for more advanced techniques encountered later on the course. We will cover spatial statistical techniques for both continuous and discrete data response types. We will put special emphasis on understanding mechanisms using visual tools, and…

Course Overview: This application-driven course will provide a founding in the basic theory & practice of Bayesian statistics, with a focus on MCMC modeling for ecological & epidemiological problems. Starting from a refresher on probability & likelihood, the course will take students all the way to cutting-edge applications such as state-space population modelling & spatial point-process modelling. By the end of the week, you should have a basic understanding of how common MCMC samplers work and how to program them,…

Course Overview: Phylogenetic comparative methods are commonly used nowadays to investigate how species diversification occurs and to test hypotheses about the mechanisms that drive phenotypic evolution, e.g. to model speciation and extinction, to understand why some groups are more diverse than others, to test whether phenotypic traits have evolved under neutral, directional or diversifying selection, to investigate how evolutionary rates are modified across the evolutionary history of a group etc. In all these cases, a phylogenetic hypothesis for the group…