Course Overview: This course will provide an introduction to working with real-life data typical of those encountered in the field of evolutionary biology and ecology. The course will be delivered by Dr. Luc Bussiere and Dr. Ane Timenes Laugen who are both practicing academics in the field of evolutionary biology.  This five day course will consist of series of modules (each lasting roughly half a day) covering model selection and simplification, generalised linear models, mixed effects models,  and non-linear models. Along the way you…

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 course will cover time series analysis with a particular focus on applications in ecology. All methods will be illustrated using the free, open-source software package R. Time Series data are ubiquitous in the physical sciences, and models for their behaviour enable scientists to understand temporal dynamics and predict future values. Participants will be taught a wide range of suitable time series models for both discrete and continuous time systems. The course will cover a range of techniques…

Course Overview: The course will cover the concepts and R tools that can be used to analyse spatial data in ecology covering elementary and advanced spatial analysis techniques applicable to both plants and animals. We will investigate analyses appropriate to transect (e.g. line surveys, trapping arrays), grid (e.g. occupancy surveys) and point data (e.g. telemetry). The focal questions will be on deriving species distributions, determining their environmental drivers and quantifying different types of associated uncertainty. Novel methodology for generating predictions…

This course is being repeated March 12th - 16th at SCENE field station Course Overview: The course will cover the base theory of ecological niche modelling and its main methodologies. By the end of this 5-day practical course, attendees will have the capacity to perform ecological niche models and understand their results, as well as to choose and apply the correct methodology depending on the aim of their type of study and data. Ecological niche, species distribution, habitat distribution, or…

Course Overview: Most high-throughput bioinformatics work these days takes place on the Linux command line. The programs which do the majority of the computational heavy lifting — genome assemblers, read mappers, and annotation tools — are designed to work best when used with a command-line interface. Because the command line can be an intimidating environment, many biologists learn the bare minimum needed to get their analysis tools working. This means that they miss out on the power of Linux to…

Course Overview: The term ‘landscape genetics’ has been applied studies that integrate ecological context and intervening landscape into population genetic analyses of contemporary processes such as gene flow and migration.  This course will cover the basics of both quantitative landscape ecology and population genetics, focusing on how we develop and evaluate spatial/genetic analyses using the R platform. Intended Audience This course is suitable for graduate students, postdoctoral researchers, and primary investigators interested in learning how to integrate landscape ecological 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: 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: This course will provide an introduction to working with real-life data typical of those encountered in the field of evolutionary biology and ecology. The course will be delivered by Dr. Luc Bussiere, Dr. Tom Houslay and Dr. Ane Timenes Laugen who are all practicing academics in the field of evolutionary biology.  This five day course will consist of series of modules (each lasting roughly half a day) covering model selection and simplification, generalised linear models, mixed effects models,  and non-linear models. Along…

Course Overview: This course will cover introductory hierarchical modelling for real-world data sets from a Bayesian perspective. These methods lie at the forefront of statistics research and are a vital tool in the scientist’s toolbox. The course focuses on introducing concepts and demonstrating good practice in hierarchical models. All methods are demonstrated with data sets which participants can run themselves. Participants will be taught how to fit hierarchical models using the Bayesian modelling software Jags and Stan through the R…

Course Overview: The main objectives of the course are to teach the theoretical bases of phylogenetic analysis, and to give the ability to initiate a phylogenetic analysis starting from the files of molecular sequences until the interpretation of the results and the graphics. The introduction will cover a brief historical background and an overview of the different methods of phylogenetic inference. Different kinds of data will be considered, but with a special emphasis on DNA sequences. The software used will…

Course Overview: The course will cover the concepts, technology and software tools that can be used to analyse movement data (from ringing/CMR to VHF/GPS to novel biologging data) in ecology and evolution, covering analysis techniques applicable broadly to most taxa, from micro-organisms to plants and animals. We will cover elementary and advanced analysis and modelling techniques broadly applicable across taxa, highlighting the advantages of a unified Movement Ecology framework. We will provide the necessary bases in ecology (especially behavioural ecology),…

Course Overview: Marxan (Ian Ball and Hugh Possingham, 2000) is a software program used to support the design of marine and terrestrial reserves.  Marxan is now used by over 6000 people in 150 countries, making it the most the most widely used conservation planning tool in the world. In simple terms, Marxan analyzes scientific and geographic data to help users allocate space in ways that maximize values such as biodiversity, land value, cultural significance, and economic productivity whilst minimizing cost.…

Course Overview: The course will cover the base theory of ecological niche modelling and its main methodologies. By the end of this 5-day practical course, attendees will have the capacity to perform ecological niche models and understand their results, as well as to choose and apply the correct methodology depending on the aim of their type of study and data. Ecological niche, species distribution, habitat distribution, or climatic envelope models are different names for similar mechanistic or correlative models, empirical…

Course Overview: This 5-day course will involve a combination of lectures and practical sessions. Students will learn to build and fit custom models for analysing behavioural data using maximum likelihood techniques in R. This flexible approach allows a researcher to a) use a statistical model that directly represents their hypothesis, in cases where standard models are not appropriate and b) better understand how standard statistical models (e.g. GLMs) are fitted, many of which are fitted by maximum likelihood. Students will…