Course Overview: Survival analysis is a set of statistical methods initially designed to analyse data giving the times at which individuals die, and assess the effect that different predictor variables have on the rate of death. However, its applications are much broader than this: it can be used to analyse any time-to-event data. Ecologists and evolutionary biologists often encounter data of this kind. Often factors influencing survival itself will be of interest. But there are many other cases, e.g. what…

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: Acoustic telemetry has become increasingly popular as a method of choice for monitoring the movements and behaviour of aquatic animals globally.  Increasingly smaller tags along with improvements in battery technology have allowed for tagging a wide variety of species and life stages, enabling monitoring of individuals as small as salmon smolts and as large as whale sharks for periods from 30 days to 10 years.  Receiver technology also continues to evolve and increasingly allows for data to be…

Course Overview: This course aims to help you to draw better statistical inferences from empirical research, improve the statistical questions you ask when you collect data, design better and more efficient studies, and improve your meta-analytic thinking. In practical, hands on assignments, you will learn techniques and tools that can be immediately implemented in your own research, such as thinking about the smallest effect size you are interested in, justifying your sample size, evaluate findings in the literature while keeping…

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: This course will introduce and explain the different applications for bioacoustics to answer ecological questions. Starting with a detailed overview of the correct and most efficient methods of data collecting in the field, this course will then go on to show delegates cutting edge methods for analysing and interpreting different types of bioacoustic data. By the end of this 5-day practical course, attendees will have the capacity to set up and deploy recording devices, download acoustic data, how…

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 course will provide attendees with the opportunity to learn how (a) to understand and read modern statistics reported in scientific studies, (b) use basic modern statistics for analysing their own data, using the open access R statistical software. The course will focus more on data deriving from life sciences namely medicine, biology, and ecology. The course will initially revise basic statistical knowledge of what is a sample and a distribution, and what is hypothesis testing. Sequentially, attendees…

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 course will provide an introduction to working with real-life data typical of those encountered in the field of psychology. The course will be delivered by Dr. Dale Barr and Dr. Luc Bussière, who are practicing academics in the fields of psychology and evolutionary biology respectively, with many years of expertise with R and statistical modelling as both scientists and instructors.  This five-day course will consist of series of modules (each lasting roughly half a day) covering topics including the…

Course Overview: This workshop will provide attendees with the opportunity to learn how (1) to understand; (2) run simulations of readily available Individual Based Models (IBMs); (3) analyze the outputs of IBMs; (4) parameterize IBMs, and (5) code relatively basic IBMs in R and NetLogo. Examples and applications will include cases from diverse domains and scientific disciplines such as social sciences, biology, and environmental sciences. Intended Audience This workshop is ideal for any scientists seeking an introduction as well as…

Course Overview: This workshop will provide attendees with the opportunity to learn how to use machine learning to analyze data coming from diverse data domains and scientific disciplines and without necessarily prior knowledge of the scientific discipline where the data are coming from. Attendees will learn how to use R and several R packages including classification and regression trees, ensembles and bagging, random forests, neural networks, and boosting (learning from mistakes). These techniques will be used for data classification, determining…

Course Overview: This course will introduce biologists and bioinformaticians to the field of comparative genomics. The course will give a deeper understanding on the advantages and disadvantages of each of approach in general to enabling an informed decision of study design for the participants for future studies and learn the cutting edge approaches to process state of the art data sets. Different techniques will be introduced to identify single nucleotide polymorphism (SNP) and structural variations (SVs) as well as the…

Course Overview: This course will provide attendees with the opportunity to learn how (a) to understand and read modern statistics reported in scientific studies, (b) use basic modern statistics for analysing their own data, using the open access R statistical software. The course will focus more on data deriving from life sciences namely medicine, biology, and ecology. The course will initially revise basic statistical knowledge of what is a sample and a distribution, and what is hypothesis testing. Sequentially, attendees…

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: This course will cover advanced statistics regarding the analysis of data using comprehensive and state-of-the-art techniques. All methods will be illustrated using the free, open-source software package R. The course is designed for attendees that have a basic knowledge of R and elementary statistics and wish to advance this to up-to-date journal publication or conference proceedings standards. The course will cover mixed effects models, model selection and multi-model inference, generalized linear models (i.e. analysis of non-normally distributed datasets),…

Course Overview: Local adaptation to climate and other environmental drivers increasingly is being studied at the molecular level using high-throughput sequencing methods, with applications spanning both model and non-model organisms. At the same time, statistical tools for modeling and mapping patterns of biodiversity have seen increasing application, including to the challenge of understanding the drivers of spatial variation in adaptive genomic variation and mapping these patterns under current and future climate. This 5-day course will provide the skill set necessary…