Introduction to Bayesian Hierarchical Modelling
2036-05-23 - 2036-05-25 | Recorded, United Kingdom
Standard modelling procedure for Species Distribution and Ecological Niche Modelling (SDMS01)

Event Date
Tuesday, November 18th, 2025

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Time Zone
TIME ZONE – UK (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.

About this course
This course is aimed towards researchers analysing field observations, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another, or through time over the lifespan of projects, or trying to combine legacy data sets with new data collected by recording units.
Such heterogeneities can bias analyses when data sets are integrated inadequately or can lead to information loss when filtered and standardized to common standards. Accounting for these issues is important for better inference regarding status and trend of species and communities.
Analysis of such ‘messy’ data sets need to feel comfortable with manipulating the data, need a full understanding the mechanics of the models being used (i.e. critically interpreting the results and acknowledging assumptions and limitations), and should be able to make informed choices when faced with methodological challenges.
The course emphasizes critical thinking and active learning through hands on programming exercises. We will use publicly available data sets to demonstrate the data manipulation and analysis. We will use freely available and open-source R packages.
The expected outcome of the course is a solid foundation for further professional development via increased confidence in applying these methods for field observations.
By the end of the course, participants should be able to:

Understand basic statistical concepts related to detection error
Work with field collected data and data from automated recording units (ARU)
Know packages such as unmarked, detect, bSims
Critically evaluate modelling options and assumptions using simulations
Fit N-mixture, distance sampling, and time-removal models to data

Intended Audiences

Academics and post-graduate students working on projects related to avian data
Applied researchers and analysts in public, private or third-sector organizations who need the reproducibility, speed and flexibility of a programming language such as R for analysing point count data arising from avian field surveys

Venue
Delivered remotely

Course Details
Time Zone – UK (GMT) local time
Availability – 25 places
Duration – 3 days, 4 hours per day
Contact hours – Approx. 12 hours
ECT’s – Equal to 1 ECT
Language – English

Teaching Format
Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.

Assumed quantitative knowledge
A basic understanding of statistical, mathematical and physical concepts. Specifically, generalised linear regression models, including mixed models; basic knowledge of calculus.

Assumed computer background
Familiarity with R, ability to import/export data, manipulate data frames, fit basic statistical models (up to GLM) and generate simple exploratory and diagnostic plots.

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.

All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.

A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.

Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience

Download R

Download RStudio

Download Zoom

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PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

Course Programme

Tuesday 18th
Day 1 – Classes from 13:30 – 17:30
Introduction

Introduction and background
Review of field sampling techniques
Introduction to agent-based simulations
Overview of regression techniques
Naïve estimates of occupancy and abundance
Multiple visits and N-mixture models

Wednesday 19th
Day 2 – Classes from 13:30 – 17:30
Introduction to modelling

Bird behaviour
Time-removal models
Observation process
Distance sampling
Combining removal and distance sampling (QPAD)

Thursday 20th
Day 3 – Classes from 13:30 – 17:30
Different approaches

Single visit-based approaches (N-mixture and SQPAD)
Analysing data from recording units
Multi-species models and using species traits and phylogeny
Dealing with roadside and other biases
Closing remarks

Course Instructor

Dr. Peter Solymos
Péter is an ecologist and R programmer. He has worked with continental scale data sets and developed statistical techniques for estimating population density from messy data sets. He is the author of numerous well-known R packages, including detect, dclone, vegan, and ResourceSelection. He works currently as a data scientist helping utility companies improving their outage and impact prevention practices, and is an adjunct professor at the University of Alberta in Edmonton, Canada.
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Work Homepage
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