PR Stats

Network Analysis for Ecologists (NWAE02)
2026-11-03 - 2026-11-04 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
Advanced Static Cartography in R (ASCR01)

Event Date
Monday, December 1st, 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 – Portugal (GMT+1) 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
Have you built an Ecological Niche Model? If yes, you have already encountered challenges on data preparation, or have struggled with issues in models fitting and accuracy. This course will teach you how to overcome these challenges and improve the accuracy of your ecological niche models. By the end of 5-day practical course, you will have the capacity to filter records and select your variables with variance inflation factor; to test effect of Maxent regularization parameter in models performance; to validate models performance and accuracy; to perform MESS analysis, null models, and mechanistic models, as well as to build your “virtual species”.
Ecological niche, species distribution, habitat distribution, or climatic envelope models are different names for mechanistic and correlative models, which are empirical or mathematical approaches to the ecological niche of a species. These methods relate different types of ecogeographical variables (environmental, topographical, human) to species physiological data or geographical locations, in order to identify the factors limiting and defining the species' niche. ENMs have become popular because of their efficiency in the design and implementation of conservation management.
By the end of 5-day practical course should be able to:

filter records and select your variables with variance inflation factor;
test the effect of Maxent regularization parameter in models performance;
validate models performance and accuracy;
perform MESS analysis, null models, and mechanistic models, as well as to build your “virtual species”.

Students will learn to use functions implemented in the packages “usdm”; “dismo”; “ENMEval”; “SDMvspecies”; “spThin”; and “NicheMapper” among others.

Intended Audiences
This course is orientated to PhD and MSc students, as well as other students and researchers working on biogeography, spatial ecology, or related disciplines, with experience in ecological niche models.

Venue
Delivered remotely

Course Details
Time Zone – Portugal (GMT+!) local time
Availability – 24 places
Duration – 5 days, 7 hours a day
Contact hours – Approx. 35 hours
ECT’s – Equal to 3ECT’s
Language – English

Teaching Format
The course will be mainly practical, with some theoretical lectures. All modelling processes and calculations will be performed with R, the free software environment for statistical computing and graphics (http://www.r-project.org/). Students will learn to use functions implemented in the packages “usdm”; “dismo”; “ENMEval”; “SDMvspecies”; “spThin”; and “NicheMapper” among others.

Assumed quantitative knowledge
A basic understanding of ecological niche models and biogeography in general is required, thus we will assume the attendees know how to run an ecological niche model.

Assumed computer background
Solid knowledge in Geographical Information Systems and R statistical package is necessary. It is also essential to have experience in ecological niche models. We will focus exclusively on advanced methods. If you need an introductory course on ecological niche models, please consider attending our basic course on PRStatistics (www.prstats.org).

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

Monday 1st
Day 1 – Classes from 09:30 to 17:30

ENM guide: how to model
ENM R packages.
Sources of environmental variables using geodata package.
Getting species records with geodata package.

Tuesday 2nd
Day 2 – Classes from 09:30 to 17:30

Variable selection with variance inflation factor (VIF) and usdm packages.
Choosing the correct study area.
Filtering records using usdm/spThin packages.
Choosing pseudo-absences with Biomod2 package.

Wednesday 3rd
Day 3 – Classes from 09:30 to 17:30

Split records in training and test with ENMeval package.
Test effect of Maxent regularization parameter.<.li>
Comparing correlative models with AIC, with ENMeval package.

Thursday 4th
Day 4 – Classes from 09:30 to 17:30

MESS practice with Biomod2 package.
Validate models null models.
VirtualSpecies virtualspecies packages.

Friday 5th
Day 5 – Classes from 09:30 to 17:30

Mechanistic model NicheMapper packages.

Dr. Neftali Sillero

Neftalí Sillero works in the analysis and identification of biodiversity spatial patterns, from species to populations and individuals. For this, he uses four powerful tools to better understand how space influence biodiversity: Geographical Information Systems, Remote Sensing, Ecological Niche Modelling, and Spatial Statistics. His main areas of research are: application of new technologies on species’ distributions atlases, ecological modelling of species’ ranges, identification of biogeographical regions and species’ chorotypes, mapping and modelling road-kill hotspots, and spatial analyses of home ranges.
He has more than 10 years’ experience working in ecological niche models. He has authored >70 peer reviewed publications and he is since 2007 Chairman of the Mapping Committee of the Societas Herpetologica Europaea, where he is the PI of the NA2RE project (www.na2re.ismai.pt), the New Atlas of Amphibians and Reptiles of Europe
Personal website
Work Webpage
ResearchGate
GoogleScholar

Teaches

Ecological Niche Modelling Using R (ENMR)
Advanced Ecological Niche Modelling Using R (ANMR)
GIS And Remote Sensing Analyses With R (GARM)

Teaches

Ecological Niche Modelling Using R (ENMR)
Advanced Ecological Niche Modelling Using R (ANMR)
GIS And Remote Sensing Analyses With R (GARM)

Dr. Salvador Arenas-Castro

Dr. Salvador Arenas-Castro is a broad-spectrum ecologist with interesting in differentintegrative perspective of the fundamental ecology, macroecology and biogeographywith their both application and relationship to climate and land management. He is alsoexploring other research sources in agroecology, forestry, spatial ecology, andecoinformatics, all addressed by explicitly considering the spatial component ofecological processes, mainly applying spatially explicit modelling approaches, GIS andremote sensing techniques. Please check his webpage for further information:https://salvadorarenascastro.wordpress.com
Google Scholar: https://scholar.google.com/citations?user=UAYiB5UAAAAJ&hl=es&oi=aoResearchGate: https://www.researchgate.net/profile/Salvador-Arenas-Castro

Bioacoustics Data Analysis (BIAC06)
2026-10-19 - 2026-10-23 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
Advanced Static Cartography in R (ASCR01)

Event Date
Monday, October 6th, 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
The study of animal acoustic signals is a central tool for many fields in behavior, ecology, evolution and biodiversity monitoring. The accessibility of recording equipment and growing availability of open-access acoustic libraries provide an unprecedented opportunity to study animal acoustic signals at large temporal, geographic and taxonomic scales. However, the diversity of analytical methods and the multidimensionality of these signals posts significant challenges to conduct analyses that can quantify biologically meaningful variation. The recent development of acoustic analysis tools in the R programming environment provides a powerful means for overcoming these challenges, facilitating the gathering and organization of large acoustic data sets and the use of more elaborated analyses that better fit the studied acoustic signals and associated biological questions. The course will introduce students on the basic concepts in animal acoustic signal research as well as hands-on experience on analytical tools in R.
By the end of the course, participants should be able to:

Understand the basic concepts of bioacoustics and how animal acoustic signals are analyzed
Gain proficiency in handling and manipulating acoustic data in R, including working with ‘wave’ objects and other audio formats
Develop skills in building and interpreting spectrograms using Fourier transform techniques and the seewave package in R
Import Raven Pro annotations into R and refine these annotations with warbleR functions
Understand how to quantify the structure of acoustic signals through various approaches
Gain experience in quality control of recordings and annotations, ensuring data integrity and accuracy
Compare different methods for quantifying acoustic signal structure and understand the implications of each approach

Intended Audiences

Academics and post-graduate students conducting research in bioacoustics, animal behavior, ecology, or related fields
Applied researchers and analysts in public, private, or non-profit organizations who require robust, reproducible, and flexible tools for analyzing acoustic data
Current R users seeking to expand their knowledge into the field of bioacoustics and learn how to utilize specialized packages for acoustic analysis
Wildlife biologists, and conservationists interested in leveraging bioacoustic methods for species monitoring and behavioral studies
Data scientists and programmers interested in applying their coding skills to the analysis of animal acoustic signals

Venue
Delivered remotely

Course Details
Time Zone – UK (GMT) local time
Availability – 20 places
Duration – 5 days, 4 hours per day
Contact hours – Approx. 20 hours
ECT’s – Equal to 2 ECT’s
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 concepts. Specifically, generalised linear regression models, statistical significance, hypothesis testing.

Assumed computer background
Familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models & 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

Tickets

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ASCR01 ONLINE

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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

Monday 6th
Day 1 – Classes from 13:30 – 17:30
Introduction

How animal acoustic signals look like?An overview of the variety of acoustic signals produced by animals, with examples from different species. This includes visualizing sound waves and spectrograms to understand their structure and complexity.
Analytical workflow in bioacoustics researchIntroduction to the step-by-step process involved in bioacoustic research, from recording anddata collection to analysis and interpretation. This session will outline the typical workflow,emphasizing the importance of each step.
Advantages of programmingDiscussion on the benefits of using programming languages like R for bioacoustic analysis,including reproducibility, efficiency, and the ability to handle large datasets. This will highlighthow programming can enhance research capabilities.

What is sound?

Sound as a time seriesExplanation of how sound can be represented as a time series, with each point in the seriesrepresenting the sound pressure level at a given moment in time. This forms the basis for further analysis and manipulation.
Sound as a digital objectDiscussion on the digitization of sound, including sampling rates, bit depth, and the conversion of analog sound waves into digital formats that can be analyzed using software.
Acoustic data in RIntroduction to handling and analyzing acoustic data in R. This includes importing sound files, basic data exploration, and visualization techniques.
‘wave’ object structureExplanation of the ‘wave’ object in R, its structure, and the information it contains. This isessential for understanding how to manipulate and analyze sound data in R.
‘wave’ object manipulationsTechniques for manipulating ‘wave’ objects, including trimming, concatenating, and modifying sound files. Practical exercises will be provided to reinforce these concepts.
Additional formatsOverview of other audio file formats (e.g., MP3, FLAC) and how they can be converted and used in R for bioacoustic analysis.

Tuesday 7th
Day 2 – Classes from 13:30 – 17:30
Building spectrograms

Fourier transformExplanation of the Fourier transform and its application in converting time-domain signals intofrequency-domain representations. This is the foundation for creating spectrograms.
Building a spectrogramStep-by-step guide on how to construct spectrograms, including parameter selection (e.g.,window size, overlap) and interpretation of the resulting visual representations.
Characteristics and limitationsDiscussion on the strengths and limitations of spectrograms, including resolution trade-offs and potential artifacts. Participants will learn to critically evaluate spectrograms.
Spectrograms in RPractical session on generating and customizing spectrograms in R using the seewave package.
Participants will create spectrograms from their own data.Package seewave
Explore, modify and measure ‘wave’ objectsHands-on exploration of the seewave package, focusing on functions for modifying andmeasuring 'wave' objects. This includes exercises on filtering, re-sampling, and extracting acoustic features.
Spectrograms and oscillogramsCreating and interpreting both spectrograms and oscillograms in R. Participants will learn tovisualize sound data in different ways to highlight various aspects of the signal.
Filtering and re-samplingTechniques for filtering (e.g., band-pass, high-pass) and re-sampling sound files to focus onspecific frequency ranges or standardize sampling rates.
Acoustic measurementsUsing the seewave package to perform detailed acoustic measurements, such as peak frequency, dominant frequency, and frequency range. Practical examples will be provided.

Wednesday 8th
Day 3 – Classes from 13:30 – 17:30
Annotations

Introduction to the Raven Pro InterfaceA guided tour of the Raven Pro software, its main features, and interface elements. Participants will learn how to navigate the software efficiently.
Introduction to selections and measurementsInstruction on how to make selections within sound files and take basic measurements such as duration and frequency using Raven Pro.
Saving, retrieving, and exporting selection tablesHow to save, retrieve, and export selection tables in Raven Pro for further analysis. This session will cover best practices for data management and organization.
Using annotationsTechniques for annotating sound files in Raven Pro, including the use of labels and notes to mark significant events or features within the recordings.

Quantifying acoustic signal structure

Spectro-temporal measurements (spectro_analysis())Introduction to the spectro_analysis() function in R for extracting spectro-temporalmeasurements from audio recordings. Participants will learn to describe acoustic signals in terms of their temporal and spectral characteristics.
Parameter descriptionDetailed explanation of key acoustic parameters, such as duration, frequency range, andamplitude, and how they are used to describe sound signals.
Harmonic contentTechniques for analyzing the harmonic content of signals, including identifying harmonic series and measuring harmonic-to-noise ratios.
Cepstral coefficients (mfcc_stats())Introduction to Mel-frequency cepstral coefficients (MFCCs) and their use in characterizing the timbral properties of sound signals. Participants will use the mfcc_stats() function to extract MFCCs.
Cross-correlation (cross_correlation())Explanation of cross-correlation techniques for comparing sound signals. Participants will use cross_correlation() to measure the similarity between different recordings.
Dynamic time warping (freq_DTW())Introduction to dynamic time warping (DTW) and its application in aligning and comparing time-series data. The freq_DTW() function will be used to compare sound signals.
Signal-to-noise ratio (sig2noise())Techniques for calculating the signal-to-noise ratio (SNR) of recordings, which is crucial forassessing the quality of sound data.
Inflections (inflections())Identifying and measuring inflections in sound signals, which can indicate changes in pitch or other dynamic features.
Parameters at other levels (song_analysis())Exploring acoustic parameters at higher hierarchical levels, such as entire songs or sequences of vocalizations, using the song_analysis() function.

Thursday 9th
Day 4 – Classes from 13:30 – 17:30
Quality control in recordings and annotations

Create catalogsCompiling catalogs of annotated sound files, which can be used for further analysis or asreference materials.
Check and modify sound file format (check_wavs(), info_wavs(), duration_wavs(),mp32wav() y fix_wavs())Techniques for checking and modifying sound file formats using various functions in R. Thisincludes converting files, checking file integrity, and fixing common issues.
Tuning spectrogram parameters (tweak_spectro())Adjusting spectrogram parameters to optimize the visualization and analysis of sound signals.Participants will use tweak_spectro() to fine-tune their spectrograms.
Double-checking selection tables (check_sels(), spectrograms(), full_spectrograms() &catalog())Methods for verifying and refining selection tables, ensuring that all annotations are accurate and comprehensive.
Re-adjusting selections (tailor_sels())Techniques for re-adjusting selections in response to quality control checks, ensuring that allannotations are precise and correctly positioned.Characterizing hierarchical levels in acoustic signals
Creating ‘song’ spectrograms (full_spectrograms(), spectrograms())Building spectrograms that represent entire songs or sequences of vocalizations, providing ahigher-level view of acoustic patterns.
‘Song’ parameters (song_analysis())Measuring and analyzing parameters at the song level, such as song duration, number ofelements and element rate, using the song_analysis() function.

Friday 10th
Day 5 – Classes from 13:30 – 17:30
Choosing the right method for quantifying structure

Compare different methods for quantifying structure (compare_methods())Comparing various methods for quantifying acoustic signal structure. Participants will usecompare_methods() to evaluate different approaches.Quantifying acoustic spaces
Intro to PhenotypeSpaceIntroduction to the concept of acoustic spaces and the PhenotypeSpace framework, which allows for the visualization and comparison of acoustic diversity.
Quantifying space sizeTechniques for measuring the size of acoustic spaces, which can provide insights into thevariability and complexity of vocalizations.
Comparing sub-spacesMethods for comparing different sub-spaces within the overall acoustic space, allowing for the analysis of variations between species, populations, or other groups.
Each of these topics will be covered with detailed explanations, practical examples, and hands-on exercises to ensure that participants gain a comprehensive understanding of bioacoustics research using the R platform.

Course Instructor

*
Dr. Marcelo Araya Salas
Works at – Neuroscience Research Center, Universidad de Costa Rica
Marcelo Araya-Salas works at the intersection of scientific programming and evolutionary behavioral ecology, focusing on the evolution of behavior and the factors influencing it across cultural and evolutionary timescales. His research primarily examines the communication systems of neotropical species using single-species behavioral studies, comparative phylogenetic methods, and advanced data analysis techniques. He has developed several computational tools for biological data analysis, including the R packages warbleR, Rraven and baRulho which simplify the manipulation of annotated acoustic data and the quantification of structure and degradation of animal sounds.
ResearchGate
Google Scholar
Work Homepage
Personal Homepage

Reproducible Spatial Ecology: Visualization, Reporting, and AI-Assisted Workflows with R (RSPE01)
2026-07-13 - 2026-07-16 | Delivered remotely (Portugal), Portugal

Advanced Static Cartography in R (ASCR01)
2026-06-03 - 2026-06-05 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
Advanced Static Cartography in R (ASCR01)

Event Date
Monday, December 1st, 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 – Portugal (GMT+1) 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
Have you built an Ecological Niche Model? If yes, you have already encountered challenges on data preparation, or have struggled with issues in models fitting and accuracy. This course will teach you how to overcome these challenges and improve the accuracy of your ecological niche models. By the end of 5-day practical course, you will have the capacity to filter records and select your variables with variance inflation factor; to test effect of Maxent regularization parameter in models performance; to validate models performance and accuracy; to perform MESS analysis, null models, and mechanistic models, as well as to build your “virtual species”.
Ecological niche, species distribution, habitat distribution, or climatic envelope models are different names for mechanistic and correlative models, which are empirical or mathematical approaches to the ecological niche of a species. These methods relate different types of ecogeographical variables (environmental, topographical, human) to species physiological data or geographical locations, in order to identify the factors limiting and defining the species' niche. ENMs have become popular because of their efficiency in the design and implementation of conservation management.
By the end of 5-day practical course should be able to:

filter records and select your variables with variance inflation factor;
test the effect of Maxent regularization parameter in models performance;
validate models performance and accuracy;
perform MESS analysis, null models, and mechanistic models, as well as to build your “virtual species”.

Students will learn to use functions implemented in the packages “usdm”; “dismo”; “ENMEval”; “SDMvspecies”; “spThin”; and “NicheMapper” among others.

Intended Audiences
This course is orientated to PhD and MSc students, as well as other students and researchers working on biogeography, spatial ecology, or related disciplines, with experience in ecological niche models.

Venue
Delivered remotely

Course Details
Time Zone – Portugal (GMT+!) local time
Availability – 24 places
Duration – 5 days, 7 hours a day
Contact hours – Approx. 35 hours
ECT’s – Equal to 3ECT’s
Language – English

Teaching Format
The course will be mainly practical, with some theoretical lectures. All modelling processes and calculations will be performed with R, the free software environment for statistical computing and graphics (http://www.r-project.org/). Students will learn to use functions implemented in the packages “usdm”; “dismo”; “ENMEval”; “SDMvspecies”; “spThin”; and “NicheMapper” among others.

Assumed quantitative knowledge
A basic understanding of ecological niche models and biogeography in general is required, thus we will assume the attendees know how to run an ecological niche model.

Assumed computer background
Solid knowledge in Geographical Information Systems and R statistical package is necessary. It is also essential to have experience in ecological niche models. We will focus exclusively on advanced methods. If you need an introductory course on ecological niche models, please consider attending our basic course on PRStatistics (www.prstats.org).

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

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

ASCR01 ONLINE

ASCR01 ONLINE

£
350.00

Unlimited

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Quantity:
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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

Monday 1st
Day 1 – Classes from 09:30 to 17:30

ENM guide: how to model
ENM R packages.
Sources of environmental variables using geodata package.
Getting species records with geodata package.

Tuesday 2nd
Day 2 – Classes from 09:30 to 17:30

Variable selection with variance inflation factor (VIF) and usdm packages.
Choosing the correct study area.
Filtering records using usdm/spThin packages.
Choosing pseudo-absences with Biomod2 package.

Wednesday 3rd
Day 3 – Classes from 09:30 to 17:30

Split records in training and test with ENMeval package.
Test effect of Maxent regularization parameter.<.li>
Comparing correlative models with AIC, with ENMeval package.

Thursday 4th
Day 4 – Classes from 09:30 to 17:30

MESS practice with Biomod2 package.
Validate models null models.
VirtualSpecies virtualspecies packages.

Friday 5th
Day 5 – Classes from 09:30 to 17:30

Mechanistic model NicheMapper packages.

Dr. Neftali Sillero

Neftalí Sillero works in the analysis and identification of biodiversity spatial patterns, from species to populations and individuals. For this, he uses four powerful tools to better understand how space influence biodiversity: Geographical Information Systems, Remote Sensing, Ecological Niche Modelling, and Spatial Statistics. His main areas of research are: application of new technologies on species’ distributions atlases, ecological modelling of species’ ranges, identification of biogeographical regions and species’ chorotypes, mapping and modelling road-kill hotspots, and spatial analyses of home ranges.
He has more than 10 years’ experience working in ecological niche models. He has authored >70 peer reviewed publications and he is since 2007 Chairman of the Mapping Committee of the Societas Herpetologica Europaea, where he is the PI of the NA2RE project (www.na2re.ismai.pt), the New Atlas of Amphibians and Reptiles of Europe
Personal website
Work Webpage
ResearchGate
GoogleScholar

Teaches

Ecological Niche Modelling Using R (ENMR)
Advanced Ecological Niche Modelling Using R (ANMR)
GIS And Remote Sensing Analyses With R (GARM)

Teaches

Ecological Niche Modelling Using R (ENMR)
Advanced Ecological Niche Modelling Using R (ANMR)
GIS And Remote Sensing Analyses With R (GARM)

Dr. Salvador Arenas-Castro

Dr. Salvador Arenas-Castro is a broad-spectrum ecologist with interesting in different
integrative perspective of the fundamental ecology, macroecology and biogeography
with their both application and relationship to climate and land management. He is also
exploring other research sources in agroecology, forestry, spatial ecology, and
ecoinformatics, all addressed by explicitly considering the spatial component of
ecological processes, mainly applying spatially explicit modelling approaches, GIS and
remote sensing techniques. Please check his webpage for further information:
https://salvadorarenascastro.wordpress.com
Google Scholar: https://scholar.google.com/citations?user=UAYiB5UAAAAJ&hl=es&oi=ao
ResearchGate: https://www.researchgate.net/profile/Salvador-Arenas-Castro

Introduction to Processing and Analysis of Spatial Multiplexed Proteomics Data (SPMP02)
2026-06-01 - 2026-06-05 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
Advanced Static Cartography in R (ASCR01)

Event Date
Monday, October 6th, 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
The study of animal acoustic signals is a central tool for many fields in behavior, ecology, evolution and biodiversity monitoring. The accessibility of recording equipment and growing availability of open-access acoustic libraries provide an unprecedented opportunity to study animal acoustic signals at large temporal, geographic and taxonomic scales. However, the diversity of analytical methods and the multidimensionality of these signals posts significant challenges to conduct analyses that can quantify biologically meaningful variation. The recent development of acoustic analysis tools in the R programming environment provides a powerful means for overcoming these challenges, facilitating the gathering and organization of large acoustic data sets and the use of more elaborated analyses that better fit the studied acoustic signals and associated biological questions. The course will introduce students on the basic concepts in animal acoustic signal research as well as hands-on experience on analytical tools in R.
By the end of the course, participants should be able to:

Understand the basic concepts of bioacoustics and how animal acoustic signals are analyzed
Gain proficiency in handling and manipulating acoustic data in R, including working with ‘wave’ objects and other audio formats
Develop skills in building and interpreting spectrograms using Fourier transform techniques and the seewave package in R
Import Raven Pro annotations into R and refine these annotations with warbleR functions
Understand how to quantify the structure of acoustic signals through various approaches
Gain experience in quality control of recordings and annotations, ensuring data integrity and accuracy
Compare different methods for quantifying acoustic signal structure and understand the implications of each approach

Intended Audiences

Academics and post-graduate students conducting research in bioacoustics, animal behavior, ecology, or related fields
Applied researchers and analysts in public, private, or non-profit organizations who require robust, reproducible, and flexible tools for analyzing acoustic data
Current R users seeking to expand their knowledge into the field of bioacoustics and learn how to utilize specialized packages for acoustic analysis
Wildlife biologists, and conservationists interested in leveraging bioacoustic methods for species monitoring and behavioral studies
Data scientists and programmers interested in applying their coding skills to the analysis of animal acoustic signals

Venue
Delivered remotely

Course Details
Time Zone – UK (GMT) local time
Availability – 20 places
Duration – 5 days, 4 hours per day
Contact hours – Approx. 20 hours
ECT’s – Equal to 2 ECT’s
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 concepts. Specifically, generalised linear regression models, statistical significance, hypothesis testing.

Assumed computer background
Familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models & 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

Monday 6th
Day 1 – Classes from 13:30 – 17:30
Introduction

How animal acoustic signals look like?
An overview of the variety of acoustic signals produced by animals, with examples from different species. This includes visualizing sound waves and spectrograms to understand their structure and complexity.
Analytical workflow in bioacoustics research
Introduction to the step-by-step process involved in bioacoustic research, from recording and
data collection to analysis and interpretation. This session will outline the typical workflow,
emphasizing the importance of each step.
Advantages of programming
Discussion on the benefits of using programming languages like R for bioacoustic analysis,
including reproducibility, efficiency, and the ability to handle large datasets. This will highlight
how programming can enhance research capabilities.

What is sound?

Sound as a time series
Explanation of how sound can be represented as a time series, with each point in the series
representing the sound pressure level at a given moment in time. This forms the basis for further analysis and manipulation.
Sound as a digital object
Discussion on the digitization of sound, including sampling rates, bit depth, and the conversion of analog sound waves into digital formats that can be analyzed using software.
Acoustic data in R
Introduction to handling and analyzing acoustic data in R. This includes importing sound files, basic data exploration, and visualization techniques.
‘wave’ object structure
Explanation of the ‘wave’ object in R, its structure, and the information it contains. This is
essential for understanding how to manipulate and analyze sound data in R.
‘wave’ object manipulations
Techniques for manipulating ‘wave’ objects, including trimming, concatenating, and modifying sound files. Practical exercises will be provided to reinforce these concepts.
Additional formats
Overview of other audio file formats (e.g., MP3, FLAC) and how they can be converted and used in R for bioacoustic analysis.

Tuesday 7th
Day 2 – Classes from 13:30 – 17:30
Building spectrograms

Fourier transform
Explanation of the Fourier transform and its application in converting time-domain signals into
frequency-domain representations. This is the foundation for creating spectrograms.
Building a spectrogram
Step-by-step guide on how to construct spectrograms, including parameter selection (e.g.,
window size, overlap) and interpretation of the resulting visual representations.
Characteristics and limitations
Discussion on the strengths and limitations of spectrograms, including resolution trade-offs and potential artifacts. Participants will learn to critically evaluate spectrograms.
Spectrograms in R
Practical session on generating and customizing spectrograms in R using the seewave package.
Participants will create spectrograms from their own data.
Package seewave
Explore, modify and measure ‘wave’ objects
Hands-on exploration of the seewave package, focusing on functions for modifying and
measuring 'wave' objects. This includes exercises on filtering, re-sampling, and extracting acoustic features.
Spectrograms and oscillograms
Creating and interpreting both spectrograms and oscillograms in R. Participants will learn to
visualize sound data in different ways to highlight various aspects of the signal.
Filtering and re-sampling
Techniques for filtering (e.g., band-pass, high-pass) and re-sampling sound files to focus on
specific frequency ranges or standardize sampling rates.
Acoustic measurements
Using the seewave package to perform detailed acoustic measurements, such as peak frequency, dominant frequency, and frequency range. Practical examples will be provided.

Wednesday 8th
Day 3 – Classes from 13:30 – 17:30
Annotations

Introduction to the Raven Pro Interface
A guided tour of the Raven Pro software, its main features, and interface elements. Participants will learn how to navigate the software efficiently.
Introduction to selections and measurements
Instruction on how to make selections within sound files and take basic measurements such as duration and frequency using Raven Pro.
Saving, retrieving, and exporting selection tables
How to save, retrieve, and export selection tables in Raven Pro for further analysis. This session will cover best practices for data management and organization.
Using annotations
Techniques for annotating sound files in Raven Pro, including the use of labels and notes to mark significant events or features within the recordings.

Quantifying acoustic signal structure

Spectro-temporal measurements (spectro_analysis())
Introduction to the spectro_analysis() function in R for extracting spectro-temporal
measurements from audio recordings. Participants will learn to describe acoustic signals in terms of their temporal and spectral characteristics.
Parameter description
Detailed explanation of key acoustic parameters, such as duration, frequency range, and
amplitude, and how they are used to describe sound signals.
Harmonic content
Techniques for analyzing the harmonic content of signals, including identifying harmonic series and measuring harmonic-to-noise ratios.
Cepstral coefficients (mfcc_stats())
Introduction to Mel-frequency cepstral coefficients (MFCCs) and their use in characterizing the timbral properties of sound signals. Participants will use the mfcc_stats() function to extract MFCCs.
Cross-correlation (cross_correlation())
Explanation of cross-correlation techniques for comparing sound signals. Participants will use cross_correlation() to measure the similarity between different recordings.
Dynamic time warping (freq_DTW())
Introduction to dynamic time warping (DTW) and its application in aligning and comparing time-series data. The freq_DTW() function will be used to compare sound signals.
Signal-to-noise ratio (sig2noise())
Techniques for calculating the signal-to-noise ratio (SNR) of recordings, which is crucial for
assessing the quality of sound data.
Inflections (inflections())
Identifying and measuring inflections in sound signals, which can indicate changes in pitch or other dynamic features.
Parameters at other levels (song_analysis())
Exploring acoustic parameters at higher hierarchical levels, such as entire songs or sequences of vocalizations, using the song_analysis() function.

Thursday 9th
Day 4 – Classes from 13:30 – 17:30
Quality control in recordings and annotations

Create catalogs
Compiling catalogs of annotated sound files, which can be used for further analysis or as
reference materials.
Check and modify sound file format (check_wavs(), info_wavs(), duration_wavs(),
mp32wav() y fix_wavs())
Techniques for checking and modifying sound file formats using various functions in R. This
includes converting files, checking file integrity, and fixing common issues.
Tuning spectrogram parameters (tweak_spectro())
Adjusting spectrogram parameters to optimize the visualization and analysis of sound signals.
Participants will use tweak_spectro() to fine-tune their spectrograms.
Double-checking selection tables (check_sels(), spectrograms(), full_spectrograms() &
catalog())
Methods for verifying and refining selection tables, ensuring that all annotations are accurate and comprehensive.
Re-adjusting selections (tailor_sels())
Techniques for re-adjusting selections in response to quality control checks, ensuring that all
annotations are precise and correctly positioned.
Characterizing hierarchical levels in acoustic signals
Creating ‘song’ spectrograms (full_spectrograms(), spectrograms())
Building spectrograms that represent entire songs or sequences of vocalizations, providing a
higher-level view of acoustic patterns.
‘Song’ parameters (song_analysis())
Measuring and analyzing parameters at the song level, such as song duration, number of
elements and element rate, using the song_analysis() function.

Friday 10th
Day 5 – Classes from 13:30 – 17:30
Choosing the right method for quantifying structure

Compare different methods for quantifying structure (compare_methods())
Comparing various methods for quantifying acoustic signal structure. Participants will use
compare_methods() to evaluate different approaches.
Quantifying acoustic spaces
Intro to PhenotypeSpace
Introduction to the concept of acoustic spaces and the PhenotypeSpace framework, which allows for the visualization and comparison of acoustic diversity.
Quantifying space size
Techniques for measuring the size of acoustic spaces, which can provide insights into the
variability and complexity of vocalizations.
Comparing sub-spaces
Methods for comparing different sub-spaces within the overall acoustic space, allowing for the analysis of variations between species, populations, or other groups.
Each of these topics will be covered with detailed explanations, practical examples, and hands-on exercises to ensure that participants gain a comprehensive understanding of bioacoustics research using the R platform.

Course Instructor

*
Dr. Marcelo Araya Salas
Works at – Neuroscience Research Center, Universidad de Costa Rica
Marcelo Araya-Salas works at the intersection of scientific programming and evolutionary behavioral ecology, focusing on the evolution of behavior and the factors influencing it across cultural and evolutionary timescales. His research primarily examines the communication systems of neotropical species using single-species behavioral studies, comparative phylogenetic methods, and advanced data analysis techniques. He has developed several computational tools for biological data analysis, including the R packages warbleR, Rraven and baRulho which simplify the manipulation of annotated acoustic data and the quantification of structure and degradation of animal sounds.
ResearchGate
Google Scholar
Work Homepage
Personal Homepage

Species Distribution Modelling With Bayesian Statistics (SDMB08)
2026-06-01 - 2026-06-03 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
Advanced Static Cartography in R (ASCR01)

Event Date
Tuesday, September 30th, 2025

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees
through the accompanying computer practicals via video link, so a good internet connection is
essential.
Time Zone
TIME ZONE – Portugal (GMT+1) 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 focuses on the use of BART (Bayesian Additive Regression Trees) for modelling
species’ geographical distributions based on occurrence data and environmental variables. BART is a relatively recent technique that shows very promising results in the field of species distribution and ecological niche modelling (SDM / ENM), as it produces accurate predictions (considering various aspects of model performance) without overfitting to noise or to special cases in the data. Additionally, BART allows mapping the uncertainty and credible intervals associated to each local prediction.
The course includes a combination of theoretical lectures and hands-on practicals in R, as well as
open discussions about models and data for SDM applications. The practicals go through a
complete worked example, from data preparation to model output analysis, with annotated R
scripts that can be adapted on-the-spot by participants to work on their own species of interest.
Along the course, the instructor is available for constant feedback and orientation on participants’; outputs and interpretations.

Intended Audiences
The course is aimed at students, researchers and practitioners with an interest in implementing
best practices and state-of-the-art methods for modelling species’ distributions or ecological
niches, in an automated and reproducible way.

Venue
Delivered remotely

Course Details
Availability – 18 places
Duration – 3 days
Contact hours – Approx. 12 hours live, plus remote assistance via Slack from the first day to the
weekday after the course.
ECT’s – Equal to 1.5 ECT’s
Language – English

Teaching Format
This course runs along 3 days, each with a 4-hour live online session. Each session is divided into
4 parts, alternating between theoretical lectures and hands-on practicals. Annotated scripts are
provided and instructor assistance is available, both during the live sessions (on Zoom) and
whenever possible the rest of the day (on Slack), until the weekday after the course.
Live sessions will be video-recorded, uploaded to a video hosting website as soon as possible after
each session, and remain available for one month after the course.

Assumed quantitative knowledge
Participants should know what species distribution or ecological niche models (SDM / ENM) are,
and ideally have some previous experience with the basics. Previous knowledge of Bayesian
statistics is not required.

Assumed computer background
Participants should have some previous experience with R, including package installation and
basic data handling, although commented scripts will be provided for the entire course.

Equipment and software requirements

Participants must use a computer with a good internet connection, a working recent version or R (and ideally also RStudio), and recent versions of some R packages whose installation instructions will be sent a few days before the course. A working webcam is desirable for enhanced interactivity during the live sessions. Some computation power is required for modelling large datasets, although the provided example data (and suggested subsets of participants’ data) can run on an ordinary laptop.

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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 30th
Day 1 – Classes from 14:00 – 18:00

Module 1a: Obtain and process data, including species presences and environmental variables
Practical
Module 1b: Determine an adequate spatial resolution and extent for modelling
Practical

Wednesday 1st
Day 2 – Classes from 14:00 – 18:00

Module 2a: Build a species distribution model with BART and obtain predictions of environmental favorability, with credibility intervals and associated uncertainty
Practical
Module 2b: Evaluate and cross-validate the model, assessing various aspects of predictive ability
Practical

Thursday 2nd
Day 3 – Classes from 14:00 – 18:00

Module 3a: Quantify variable contributions and try out different methods for selecting relevant variables
Practical
Module 3b: Plot and map the species’ partial response to each variable
Practical

Dr. Marcia Barbosa

Márcia is an experienced researcher and instructor in biogeography and macroecology, particularly in geographic information systems and species distribution modelling. She’s also a reviewer and editor for scientific journals and funding agencies, and a promoter and developer of free and open-source software implementing transparency, reproducibility and best practices. You can see her publication list at her website or at Publons/ResearcherID, Scopus, ORCID, Google Scholar, or ResearchGate.
Research Gate
Google Scholar
ORCID
GitHub
Homepage

Interactive Maps with tmap & Shiny (IMTS01)
2026-05-27 - 2026-05-29 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom

Species Distribution Modelling (SDMs) and Ecological Niche Modelling (ENMs) (SDMR07)
2026-05-11 - 2026-05-15 | Delivered remotely (Portugal), Portugal
Advanced Static Cartography in R (ASCR01)

Event Date
Monday, September 22nd, 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 – Portugal (GMT+1) 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
How to build an ecological niche model – ENM? This course covers the fundamental theory and principal methodologies used to build Ecological Niche Models (ENMs). These models, which may also be referred to as species distribution models (SDMs), habitat suitability models, or climate envelope models, represent empirical or mathematical approaches to understanding a species’ ecological niche. ENM techniques can be broadly categorised as mechanistic or correlative. They function by relating known species information (such as geographical locations or physiological data) with various types of ecogeographical variables, including environmental (e.g., climate), topographical (e.g., elevation), and human factors. The ultimate goal is to identify the conditions and factors that limit and define the species’ niche. The increasing popularity of ENMs stems from their utility in making conservation planning and management more effective and efficient.
By the end of the course, participants should be able to:

Calculate ecological niche models and specie distribution models
Understand their results, as well as to choose and apply the correct.
How to choose the best methodology depending on the aim of their type of study and data.

Intended Audiences

Academics and post-graduate students working on projects related to spatial data.
Students and researchers working on biogeography, spatial ecology, or related disciplines with experience in ecological niche models.

Venue
Delivered remotely

Course Details
Time Zone – Portugal (GMT+1) local time
Availability – 30 Places
Duration – 5 days, 7 hours a day
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
The morning of the first day will be mainly theoretical. The following days will be mainly practical, with some short theoretical presentations. All modelling processes and calculations will be performed with R, the free software environment for statistical computing and graphics (http://www.r-project.org/). Attendees will learn to use modelling algorithms like Maxent, Bioclim, Domain, and logistic regressions, and R packages for computing ENMs like Dismo and Biomod2. Also, students will learn to compare different ecological niche models using the Ecospat package. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data. In the final practical, the students will run ENM with their own data or with a new dataset, applying all the methods shown during the previous days.

Assumed quantitative knowledge
A basic understanding of statistical and ecological concepts.

Assumed computer background
Solid knowledge in Geographical Information Systems and the R statistical package is necessary. We will focus exclusively on advanced methods. If you need an introductory course on ecological niche models, please consider attending our basic course on PRStatistics (www.prstatistics.com).

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

Monday 22nd
Day 1 – Classes from 09:30 to 17:30

Topic 1: Introduction to ENM theory. Definition of ecological niche model; introduction to species ecological niche theory, types of ecological niches, types of ENM, diagram BAM, ENMs as approximations to species’ niches.
Topic 2: ENM methods. Mechanistic and correlative models. Overlap Analysis, Biomod, Domain, Habitat, Distance of Mahalanobis, ENFA, Maxent, Logistic regression, Generalised Linear Models, Generalised Additive Models, Generalised Boosted Regression Models, Random Forest, Support Vector Machines, Artificial Neural Network.
Topic 3: Preparing variables and species data. Getting climatic data from WorldClim and species data from the Global Biodiversity Information Facility using the geodata package. Choosing environmental data sources, downloading variables, Clipping variables, Aggregating variables, checking pixel size, checking raster limits, checking NoData, Correlating variables.

Tuesday 23rd
Day 2 – Classes from 09:30 to 17:30

Topic 4: Guidelines to calculate ENM. Concepts of ecological niche and how they can be modelled; classes of correlative models; modelling software; selection of study area; data sources for species records and environmental variables; types of species records and their influence on correlative models; errors in species records; minimum number of species records and environmental variables; effects of prevalence, sampling design, biases, and collinearity between variables; model calculation; model projection to different scenarios in time and space; ensemble modelling; model validation; classification, discrimination and calibration metrics; calculation of null models; analysis of model results; and model thresholding.
Topic 5: Modelling with the predicts package. Formatting the data, parameterising the modelling correlative algorithms, calculating the models, evaluating the models, projecting the models over time and space.

Wednesday 24th
Day 3 – Classes from 09:30 to 17:30

Topic 6: Applications of ENM. Ecological niche identification, Identification of contact zones, Integration with genetical data, Species expansions, Species invasions, Dispersion hypotheses, Species conservation status, Prediction of future conservation problems, Projection to future and past climate change scenarios, Modelling past species, Modelling species richness, Road-kills, Diseases, Windmills, Location of protected areas.
Topic 7: Modelling with the biomod2 package. Formatting the data, parameterising the modelling correlative algorithms, calculating the models, evaluating the models, projecting the models over time and space.

Thursday 25th
Day 4 – Classes from 09:30 to 17:30

Topic 8: Modelling with Maxent. Formatting the data, parameterising Maxent, calculating the models, evaluating the models, projecting the models over time and space.
Topic 9: Compare statistically two different ecological niche models using the R package ecospat.

Friday 26th
Day 5 – Classes from 09:30 to 17:30

Topic 10: Run ecological niche models with your own data.
Topic 11: Participants’ talks. Attendees will have the opportunity to present their own data and analyse which is the best way to successfully obtain an ENM.

Dr. Neftali Sillero

Neftalí Sillero works in the analysis and identification of biodiversity spatial patterns, from species to populations and individuals. For this, he uses four powerful tools to better understand how space influence biodiversity: Geographical Information Systems, Remote Sensing, Ecological Niche Modelling, and Spatial Statistics. His main areas of research are: application of new technologies on species’ distributions atlases, ecological modelling of species’ ranges, identification of biogeographical regions and species’ chorotypes, mapping and modelling road-kill hotspots, and spatial analyses of home ranges.
He has more than 10 years’ experience working in ecological niche models. He has authored >70 peer reviewed publications and he is since 2007 Chairman of the Mapping Committee of the Societas Herpetologica Europaea, where he is the PI of the NA2RE project (www.na2re.ismai.pt), the New Atlas of Amphibians and Reptiles of Europe
Personal website
Work Webpage
ResearchGate
GoogleScholar

Teaches

Ecological Niche Modelling Using R (ENMR)
Advanced Ecological Niche Modelling Using R (ANMR)
GIS And Remote Sensing Analyses With R (GARM)

Teaches

Ecological Niche Modelling Using R (ENMR)
Advanced Ecological Niche Modelling Using R (ANMR)
GIS And Remote Sensing Analyses With R (GARM)

Machine Learning for Time Series (MLTP01)
2026-05-11 - 2026-05-15 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
Advanced Static Cartography in R (ASCR01)

Event Date
Monday, September 22nd, 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.
Course Program
TIME ZONE – Spain (GMT+2) local time UTC+2 – 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.

Course Details
The aim of the course is to introduce you to Bayesian inference using the integrated nested Laplace approximation (INLA) method and its associated R-INLA package for the analysis of spatial and spatio-temporal data. This course will cover the basics on the INLA methodology as well as practical modelling of different types of spatial and spatio-temporaldata.
By the end of the course participants should be able to:

Know the different types of spatial and spatio-temporal data available and how to work with them in R.
Know the different modelling approaches for spatial and spatio-temporal data.
Know how to visualize and produce maps of spatial and spatio-temporal data.
Be able to fit models with the R-INLA package.
Know how to interpret the output from model fitting.
Be confident with the use of INLA for data analysis.
Understand the different models that can be fit with INLA to spatial and spatio-temporal data.
Know how to define the different parts of a model with INLA.
Have the confidence to use INLA for their own projects.

Intended Audiences

Academics and post-graduate students working on projects related to spatial and spatio-temporal data analysis and modelling and who want to add the INLA methodology for Bayesian inference to their toolbox.
Applied researchers and analysts in public, private or third-sector organizations who need the reproducibility, speed and flexibility of a command-line language such as R.
The course is designed for intermediate-to-advanced R users interested in data analysis and modelling. Ideally, they should have some background on probability, statistics and data analysis.

Venue
Delivered remotely

Course Information
Time zone – Spain (GMT+2) local time
Availability – 20 places
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English

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.

Teaching Format

The course will be a mixture of theoretical and practical sessions. Each concept will be first described and explained, and next there will be a time to exercise the topics using provided data sets. Participants are also very welcome to bring their own data.

Assumed quantitative knowledge
The course is designed for intermediate-to-advanced R users interested in Bayesian inference for data analysis and R beginners who have prior experience with Bayesian inference. Although an introduction to the INLA method will be given, attendants are expected to be familiar with Bayesian inference. This includes how to define simple Bayesian models and have a basic understanding of some typical methods to compute or approximate the prior distributions (such as models with conjugate priors, MCMC methods, etc.).

Assumed computer background
Attendees should already have experience with R and be familiar with data from different formats (csv, tab, etc.), create simple plots, and manipulate data frames. Furthermore, knowledge of how to fit generalized linear (mixed) models using typical R functions (such as glm and lme4) will be useful. No previous background on handling of spatial and spatio-temporal data will be assumed.

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

Tickets

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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

Monday 22nd
Day 1 – Classes from 14:00 to 21:00

Session 1 – Intro to INLA
Practical 1 – Intro to INLA
Session 2 – Model fitting with INLA
Practical 2 – Model fitting with INLA
Session 3 – GLMM’s with INLA
Practical 3 – GLMM’s with INLA
Q and A and end of day summary

Tuesday 23rd
Day 2 – Classes from 14:00 to 21:00

Session 4 – Spatial Data
Practical 4 – Spatial Data
Session 5 – Spatio-Temporal Data
Practical 5 – Spatio-Temporal Data
Session 6 – Advanced Visualisation
Practical 6 – Advanced Visualisation
Q and A and end of day summary

Wednesday 24th
Day 3 – Classes from 14:00 to 21:00

Session 7 – Spatial Models for Lattice Data
Practical 7 – Spatial Models for Lattice Data
Session 8 – Spatial Models for Continuous Data
Practical 8 – Spatial Models for Continuous Data
Session 9 – Spatial Models for Point Patterns
Practical 9 – Spatial Models for Point Patterns
Q and A and end of day summary

Thursday 25th
Day 4 – Classes from 14:00 to 21:00

Session 10 – Spatio-Temporal Models for Lattice Data
Practical 10 – Spatio-Temporal Models for Lattice Data
Session 11 – Spatio-Temporal Models for Continuous Data
Practical 11 – Spatio-Temporal Models for Continuous Data
Session 12 – Spatio-Temporal Models for Point Patterns
Practical 12 – Spatio-Temporal Models for Point Patterns
Q and A and end of day summary

Friday 26th
Day 5 – Classes from 14:00 to 21:00

Case studies, own data and problem solving.

Dr Virgillio Gomez Rubio

Virgilio has ample experience in Bayesian inference and statistical modeling as well as developing packages for the R programming language. His book Bayesian inference with INLA has been widely adopted for Bayesian modeling and it has been awarded the 2022 SEIO-BBVA Foundation Award in the category of Data Science and Big Data. You can find more information about him on here

Researchgate

Google Scholar

ORCID

GitHub

Movement Ecology (the Analysis of Movement Data)
2026-05-11 - 2026-05-15 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
Advanced Static Cartography in R (ASCR01)

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

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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.
Google Scholar
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Personal Homepage

Bayesian Statistical Modelling with Stan and brms (BMSB01)
2026-05-05 - 2026-05-06 | Delivered remotely (United Kingdom), Western European Time, United Kingdom

Bayesian Modelling Using R-INLA Course (BMIN04)
2026-05-04 - 2026-05-08 | Delivered remotely (United Kingdom), Western European Time, United Kingdom

Interactive Data Applications with Shiny (SHID01)
2026-04-28 - 2026-04-29 | Delivered remotely (United Kingdom), Western European Time, United Kingdom

Bayesian Nonlinear Models for Ecologists (BNLM01) SOLD OUT!
2026-04-27 - 2026-05-01 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom

Stable Isotope Mixing Models Using SIBER, SIAR, MixSIAR (SIMM12)
2026-04-27 00:00 - 2026-04-30 23:59 (Europe/London) | Delivered remotely (Portugal), Portugal

Mechanistic Species Distribution Modelling / Ecological Niche Modelling with NicheMapR (MSDM01)
2026-04-23 - 2026-04-24 | Delivered remotely (Portugal), Portugal

Standard modelling procedure for Species Distribution and Ecological Niche Modelling (SDMS01)
2026-04-23 00:00 - 2026-04-24 23:59 | Delivered remotely (Portugal), Portugal

Deep Learning using R (DLUR01)
2026-04-21 - 2026-04-22 | Delivered remotely (United Kingdom), Western European Time, United Kingdom

Multivariate Analysis of Ecological Communities Using VEGAN (VGNR09)
2026-04-20 - 2026-04-24 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
Advanced Static Cartography in R (ASCR01)

Event Date
Monday, September 15, 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 – Reunion (GMT+4) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow.

About This Course
This 5-day course covers R concepts, methods, and tools that can be used to analyse community ecology data using (but not limited to) the R package VEGAN. The course will review data processing techniques relevant to multivariate data sets. We will cover diversity indices, distance measures and distance-based multivariate methods, clustering, classification and ordination techniques using the R package VEGAN. We will use real-world empirical data sets to motivate analyses, such as describing patterns along gradients of environ-mental or anthropogenic disturbances, quantifying the effects of continuous and discrete predictors. We will emphasise visualisation and reproducible workflows as well as good programming practices. The modules will consist of introductory lectures, guided computer coding, and participant exercises. The course is intended for intermediate users of R who are interested in community ecology, particularly in the areas of terrestrial and wetland ecology, microbial ecology, and natural resource management. You are strongly encouraged to use your own data sets (they should be clean and already structured, see the document: “recommendation if you participate with your data”.
We will cover the following:

Fundamentals of community ecology,
Diversity indices,
Methods to transform data and calculate distance measures,
Classifications (i.e., clustering methods) organise the data into synthetic groups and present them in a tree (dendrogram).
Ordinations (i.e., unconstrained methods) reveal the multivariate dimension in only a few dimensions (axes).
Canonical ordinations (i.e., constrained methods) test hypotheses related to multivariate patterns.

In addition the course provides lectures and practices on how to create reproducible workflows and use good programming practices in R.

Topics covered during the course include: terrestrial and wetland ecology, microbial ecology, and natural resource management, evolution, palaeoecology.

During the workshops you will follow guided computer coding exercises using either your own data or real empirical datasets to motivate analyses. Exercises include describing patterns along gradients of environmental or anthropogenic disturbance, quantifying the effects of continuous and discrete predictors.

You are strongly encouraged to use your own datasets (they should be clean and already structured, please contact use if you plan to do this, we will help you to prepare the data). You will benefit from full support in applying multivariate methods to your dataset (defining of the research question, transforming your data, selecting the most appropriate method, carrying out the analysis and interpreting the results).

Intended Audiences
Any researchers (PhD and MSc students, post-docs, primary investigators) and environmental professionals who are interested in implementing best practices and state-of-the-art methods for modelling species’ distributions or ecological niches, with applications to biogeography, spatial ecology, biodiversity conservation and related disciplines.

Venue
Delivered remotely

Course Details
Time Zone – Reunion Island (GMT+4) local time
Availability – 20 places
Duration – 5 days, 8 hours a day
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English (with the option to discuss individually in French)

Teaching Format
The course will be divided into theoretical lectures to introduce and explain key concepts and theories, and practices with workshop sessions on R. We will cover roughly 2 modules per day, each module consists of ~1h30/2h lecture + coding, break, ~1h30/2h exercises + summary/discussion.
The schedule can be slightly modified according to the interest of the participants and to accommodate different timezones.

Assumed quantitative knowledge
We will assume that you are familiar with basic statistical concepts, linear models, and statistical tests (the equivalent of an undergraduate introductory statistics course will be sufficient to follow the course).

Assumed computer background
To take full advantage of this course, minimal prior experience with R is required. Participants should be familiar with basic R syntax and commands, know how to write code in the RStudio console and script editor, load data from files (txt, xls, csv).

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

Monday 15th
Day 1 – Classes from 08:00 – 13:00 and 14:00 – 16:00
• Module 1: Introduction to community data analysis, basics of programming in R
• Module 2: Diversity analysis, species-abundance distributions

Tuesday 16th
Day 2 – Classes from 08:00 – 13:00 and 14:00 – 16:00
• Module 3: Distance and transformation measures
• Module 4: Clustering and classification analysis

Wednesday 17th
Day 3 – Classes from 08:00 – 13:00 and 14:00 – 16:00
• Module 5: Unconstrained ordinations: Principal Component Analysis
• Module 6: Other unconstrained ordinations

Thursday 18th
Day 4 – Classes from 08:00 – 13:00 and 14:00 – 16:00
• Module 7: Constrained ordinations: RDA and other canonical analysis
• Module 8: Statistical tests for multivariate data and variation partitioning

Friday 19th
Day 5 – Classes from 08:00 – 13:00 and 14:00 – 16:00
• Module 9: Overview of Spatial analysis, and recent Hierarchical Modeling of Species Communities (HMSC) methods
• Modules 10: Special topics and discussion, analyzing participants’ data.

Antoine Becker-Scarpitta

Antoine is a community ecologist and forest ecologist working as a researcher at The French agricultural research and international cooperation organization, working for the sustainable development of tropical and Mediterranean regions. Antoine was a postdoctoral researcher at the University of Helsinki and the Institute of Botany of the Academy of the Czech Republic. He holds a degree in Conservation Biology from the University of Paris-Sud-Orsay, and he obtained his PhD in Biology/Ecology from the University of Sherbrooke (Canada). Antoine’s research focuses on the temporal dynamics of biodiversity, particularly on the forest and Arctic vegetation. Antoine has taught community ecology, plant ecology and evolution, linear and multivariate statistics assisted on R.
ResearchGate
Google Scholar
ORCID
GitHub

Analysing Ecological Data with Detection Error
2026-04-20 - 2026-04-23 | Delivered remotely (United Kingdom), Western European Time, United Kingdom

Deep Learning Using Python (DLUP01)
2026-04-14 - 2026-04-15 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
Advanced Static Cartography in R (ASCR01)

Event Date
Tuesday, 16th September, 2025

Course Format
This is a ‘LIVE COURSE’ – the instructors will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
COURSE PROGRAM

TIME ZONE – UK (GMT+1) 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
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 aims to give novice programmers an introduction to using Python for research in evolutionary biology and genomics by using biological examples throughout. We will use example datasets and problems themed around sequence analysis, taxonomy and ecology, with plenty of time for participants to work on their own research data.
This workshop is aimed at complete beginners and assumes no prior programming experience. 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 able to:

Apply the skills they have learned to tackling problems in their own research
Continue their Python education in a self-directed way. All course materials (including copies of presentations, practical exercises, data files, and example scripts prepared by the instructing team) will be provided electronically to participants.

Intended Audiences
This workshop is aimed at all researchers and technical workers with a background in biology who want to learn programming. The syllabus has been planned with complete beginners in mind; people with previous programming experience are welcome to attend as a refresher but may find the pace a bit slow. If in doubt, take a look at the detailed session content below or drop Martin Jones (martin@pythonforbiologists.com) an email.
Students should have enough biological background to appreciate the examples and exercise problems (i.e. they should know about DNA and protein sequences, what translation is, and what introns and exons are). No previous programming experience or computer skills (beyond the ability to use a text editor) are necessary, but you’ll need to have a laptop with Python installed.

Venue
Delivered remotely

Course Details
Time zone – UK (GMT+1) local time
Availability – 20
Duration – 4 days, 8 hours per day
Contact hours – Approx. 28 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
Lectures/discussions of Python code, libraries and techniques delivered using interactive notebooks. Workshop/practical time for students to tackle carefully designed programming challenges that use the material from the discussion sessions. Usually followed up by discussion of solutions, wrap up and summarisation.

Assumed quantative knowledge
Little technical knowledge is assumed – we will be focussing more on applied problem-solving and less on statistics, mathematics and interpretation. No maths is involved beyond basic addition/subtraction/powers etc.

Assumed computer background
This course is suitable for complete beginners; all that is necessary is admin rights on a laptop in order to be able to install software. Pre course instructions will contain links to all software and data files that are necessary.

Equipment and software requirements

A laptop computer with a working version of Python is required. Python is free and open-source software for PCs, Macs, and Linux computers.

Participants should be able to install additional software on their computers during the course (please ensure you have administration rights to your computer).

Although not absolutely necessary, a large monitor and a second screen could improve the learning experience. Participants are also encouraged to keep their webcams active to increase their interaction with the instructor and other students.
Download Python

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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 16th
Day 1 – Classes form 09:30 – 17:30
Session 1 : Introduction, environment and text manipulation

In this session I introduce the students to Python and explain what we expect them to get out of it and how learning to program can benefit their research. I explain the format of the course and take care of any housekeeping details (like coffee breaks and catering arrangements). I outline the edit-run-fix cycle of software development and talk about how to avoid common text editing errors. In this session, we also check that the computing infrastructure for the rest of the course is in place (e.g. making sure that everybody has an appropriate version of Python installed). Next, students learn to write very simple programs that produce output to the terminal, and in doing so become comfortable with editing and running Python code. This session also introduces many of the technical terms that we’ll rely on in future sessions. I run through some examples of tools for working with text and show how they work in the context of biological sequence manipulation. We also cover different types of errors and error messages, and learn how to go about fixing them methodically. Core concepts introduced: terminals, standard output, variables and naming, strings and characters, special characters, output formatting, statements, functions, methods, arguments, comments.

Session 2 : Files, slices and user interfaces

I introduce this session by talking about the importance of files in bioinformatics pipelines and workflows, and we then explore the Python interfaces for reading from and writing to files. This involves introducing the idea of types and objects, and a bit of discussion about how Python interacts with the operating system. We will also take a look at Python’s slice syntax, which will play an important role later in the course once we introduce data structures. The practical session is spent combining the techniques from the first session with the file IO tools to create basic file processing scripts. Core concepts introduced: objects and classes, paths and folders, relationships between variables and values, text and binary files, newlines.

Wednesday 17th
Day 2 – Classes form 09:30 – 17:30
Session 3 : Lists and loops

In this session we’ll start by thinking about the kinds of programs that we need to write for our research work. An important idea is that we want to write programs that can deal with arbitrary amounts of data. In order to do so, we need two things: a way of *storing* large collections of values, and a way of *processing* them. In Python, **lists** and **loops** do these jobs respectively. We’ll go over the new syntax needed for each, and see how together they allow us to write programs that are much closer to being useful in the real world. This new syntax will allow us to see how lists, strings and files all share similar behaviour and how we can take advantage of that fact to write concise code. In the practical session we’ll tackle some problems that involve larger data files. Core concepts introduced: lists and arrays, blocks and indentation, variable scoping, iteration and the iteration interface, ranges.

Session 4 : conditions and flow control

We will start this session by using the idea of decision making as a way to introduce conditional tests, and outline the different building blocks of conditions before showing how conditions can be combined in an expressive way. We look at the different ways that we can use conditions to control program flow, and how we can structure conditions to keep programs readable. These simple ideas combine with the material we have already covered to allow us to write programs that can follow rules and enforce logic. Correspondingly, in the practical session we’ll be able to attempt some complex filtering challenges on a structured CSV file. Core concepts introduced: Truth and falsehood, Boolean logic, identity and equality, evaluation of statements, branching.

Thursday 18th
Day 3 – Classes from 09:30 – 17:30
Session 5 : Organizing and structuring code

We’ll start off by discussing functions that we’d like to see in Python before considering how we can add to our computational toolbox by creating our own. We examine the nuts and bolts of writing functions before looking at best practice ways of making them usable. We also look at a couple of advanced features of Python – named arguments and defaults. This session ends with a first look at the concepts behind automated testing, and the easiest way to get started with tests in Python. The practical session makes extensive use of automated testing, with students writing functions to pass a series of unit tests. Core concepts introduced: argument passing, encapsulation, data flow through a program, unit testing.

Session 6 : The Python standard library and Regular expressions

We begin this sesion by browsing the documentation for the Python standard library and discussing how it fits in with the core parts of Python that we’ve already discussed, along with other libraries of code that students may have already encountered. To explore how Python’s module system works in detail we will take a close look at one particular module: the one that deals with regular expressions. We’ll see how a range of common problems in bioinformatics can be described in terms of pattern matching, and give an overview of Python’s regex tools. We look at the building blocks of regular expressions themselves, and learn how they are a general solution to the problem of describing patterns in strings, before practising writing some specific examples of regular expressions. Core concepts introduced: domain-specific languages, modules and namespaces.

Friday 19th
Day 4 – Classes form 09:30 – 17:30
Session 7 : Dictionaries
All of the data sets that we’ve considered so far in the course fit nicely into the list paradigm. In this session, it’s time to introduce the second major data structure offered by Python: the dictionary. To do this, we’ll look at a classic bioinformatics problem – kmer counting – and see how lists aren’t a good fit before learning the new syntax that we need to make dictionaries. Comparing the list and dictionary solutions will make it clear when we should use each approach. We’ll wrap up by discussing a few more examples of key-value data and see how the problem of storing them is a common one across bioinformatics and programming in general. In the practical session we will practice writing programs that create dictionaries, and ones that use dictionaries, including another classic bioinformatics problem: DNA to protein translation. Core concepts introduced: paired data types, hashing, key uniqueness, argument unpacking and tuples.
Session 8 : File management and housekeeping scripts
This session concerns a part of the Python standard library that is boring but useful – the modules concerned with file manipulation. We will cover the tools that Python gives us to automate the common repetitive housekeeping operations that are part of many bioinformatics projects, but rarely make it into the final publication – things like renaming, moving and deleting files, creating folders, etc. The notebook part of this session is quite brief, giving us a generous amount of practical time to tackle an example of a bioinformatics data pre-processing problem: organizing a collection of DNA sequences by length. Although the problem can be stated very concisely, we’ll quickly see that there are quite a few subtleties to it, giving us a chance to think about program state of multiple runs, processing multiple files, and creating multiple output files.

Dr. Martin Jones

Martin a freelance trainer specialising in teaching programming (mostly Python) and Linux skills to researchers in the field of biology. He trained as a biologist and completed his PhD in large-scale phylogenetics in 2007, then held a number of academic positions at the University of Edinburgh ending in a two year stint as Lecturer in Bioinformatics. I launched Python for Biologists in 2015 and have been teaching and writing full-time ever since.

Machine Learning for Ecological Time Series (METR01)
2026-04-13 - 2026-04-17 | Delivered remotely (United Kingdom), Western European Time, United Kingdom

Python for Data Science and Statistical Computing (PYDS01)
2026-04-07 - 2026-04-08 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom

Causal Inference for Ecologists (CIFE01) SOLD OUT!
2026-03-23 - 2026-03-27 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom

Advanced Python for Ecologists and Evolutionary Biologists
2026-03-23 - 2026-03-26 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
Advanced Static Cartography in R (ASCR01)

Event Date
Tuesday, 28th October, 2025

Course Format
This is a ‘LIVE COURSE’ – the instructors will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
COURSE PROGRAM

TIME ZONE – UK (GMT+1) 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
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 aims to give novice programmers an introduction to using Python for research in evolutionary biology and genomics by using biological examples throughout. We will use example datasets and problems themed around sequence analysis, taxonomy and ecology, with plenty of time for participants to work on their own research data.

Learning about advanced language features (recursion, complex data structures, comprehensions, exceptions) that are relevant to bioinformatics work
Learning about development tools (benchmarking, profiling, unit testing) that can make it easier for us to write code that is both *fast* and *correct*
Learning about different programming styles and concepts (object-oriented programming, functional programming) that are suitable for different kinds of problems.

The workshop will use examples and exercises drawn from various aspects of bioinformatics work. After completing the workshop, students should be in a position to (1) take advantage of the advanced language features in their own programs and (2) use appropriate tools when developing software programs. They will also have a deeper understanding of how Python works internally, which will be invaluable when making sense of existing code and packages.

Intended Audiences
This course is designed for people who already know some Python and who are interested in tackling more ambitious programs, particularly ones that will deal with large or complex datasets and will therefore need to work efficiently. Students should have a basic biological background (or be prepared to ask a lot of questions!) as the examples and exercises assume some knowledge of what DNA is, what is meant by gene expression, how to read a phylogenetic tree, etc.
ThIS course is **NOT** suitable for complete beginners to Python as we will assume quite a lot ofknowledge of the basic syntax of the language. The material covered in the Introduction to Python for Biologists course is a suitable background. If you are not sure about whether the course is suitable for you please email martin@pythonforbiologists.com and I will help you figure it out.

Venue
Delivered remotely

Course Details
Time zone – UK (GMT+1) local time
Availability – 20
Duration – 4 days, 8 hours per day
Contact hours – Approx. 28 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
Lectures/discussions of Python code, libraries and techniques delivered using interactivenotebooks. Workshop/practical time for students to tackle carefully designed programmingchallenges that use the material from the discussion sessions. Usually followed up bydiscussion of solutions, wrap up and summarisation.

Assumed quantative knowledge
Only descriptive statistics; the sections on performance require us to be comfortable discussing means and standard deviations.

Assumed computer background
This course assumes a background knowledge of Python syntax, so is not suitable for complete beginners to programming. If you have any questions about whether the course is suitable, don’t hesitate to email martin@pythonforbiologists.com to chat.

Equipment and software requirements

A laptop computer with a working version of Python is required. Python is free and open-source software for PCs, Macs, and Linux computers.

Participants should be able to install additional software on their computers during the course (please ensure you have administration rights to your computer).

Although not absolutely necessary, a large monitor and a second screen could improve the learning experience. Participants are also encouraged to keep their webcams active to increase their interaction with the instructor and other students.
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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 28th
Day 1 – Classes form 09:30 – 17:30
Session 1 : Recursion and trees

In this session we will cover two very closely related concepts: trees (i.e. the various ways that we can store hierarchical data) and recursive functions (the best way to operate on treelike data). As recursion is inherently confusing, ww will start with a gentle introduction using biological examples before moving on to consider a number of core tree algorithms concerning parents, children, and common ancestors. In the practical session we'll look in detail at one particular way of identifying the last common ancestor of a group of nodes, which will give us an opportunity to explore the role of recursion. Core concepts introduced: nested lists, storing hierarchical data, recursive functions, relationship between recursion and iteration.

Session 2 : Complex data structures

In this session we will briefly recap Python’s basic data structures, before looking at a couple of new data types — tuples and sets — and discussing where each should be used. We will then see how we can combine these basic types to make more complex data structures for solving specific problems. We will finish our discussion by looking at specialized data types that are found in the Python core library. This session will also be our first introduction to benchmarking as we talk about the relative performance of different data types. In the practical session we will learn how to parse an input file into a complex data structure which we can then use to rapidly query the data. Core concepts introduced: tuples, sets, higher-order data structures, default dicts, Counters, big-O notation.

Wednesday 29th
Day 2 – Classes form 09:30 – 17:30
Session 3 : Classes and objects

In this session we will introduce the core concepts of object-oriented programming, and see how the data types that we use all the time in Python are actually examples of classes. We'll take a very simple example and use it to examine how we can construct our own classes, moving from an imperative style of programming to an object-oriented style. As we do so, we'll discuss where and when object-orientation is a good idea. In the practical we will practise writing classes to solve simple biological problems and familiarize ourselves with the division of code into library and client that object-oriented programming demands. Core concepts introduced: classes, instances, methods vs. functions, self, constructors, magic methods.

Session 4 : Object-oriented programming

Following on from the previous session, we will go over some advanced ideas that are common to most object-oriented programming languages. For each idea we'll discuss the basic concept, the scenarios in which it's useful, and the details of how it works in Python. This overview will also allow us to consider the challenges involved in designing object-oriented code. In the practical we will work on a simulation which will involve multiple classes working together. Core concepts introduced: inheritance and class hierarchies, method overriding, superclasses and subclasses, polymorphism, composition, multiple inheritance.

Thursday 30th
Day 3 – Classes from 09:30 – 17:30
Session 5 : Functional programming in Python

This session will start with a look at a few different concepts that are important in functionalprogramming, culminating in a discussion of the idea of state and its role in program design. We will see how functional programming is, in many ways, the complement of object-oriented programming and how that realization informs our decision about when to use each approach. We will take a quick tour of Python's built in tools that take advantage of functional programming and see how we can build our own. We will finish with a brief look at how functional programming can vastly simplify the writing of parallel code. In the practical, we will practise using Python's built in functional tools, then implement one of our own. Core concepts introduced: state and mutability, side effects, first-class functions, declarative programming, lazy evaluation, parallelism, higher-order functions.

Session 6 : Exception handling

This session will start with a reminder of the difference between syntax errors and exceptions, after which we will explore the syntax involved in catching and handling exceptions. We will then examine the way that exceptions can be handled in multiple places and the consequences for program design. We will finish this session by learning how we can take advantage of Python’s built in exception types to signal problems in our own code, and how we can create custom exception types to deal with specific issues. In the practical we will modify existing code to make use of exceptions. Core concepts introduced: exception classes, try/except/else/finally blocks, context managers, exception bubbling, defining and raising exceptions.

Friday 31st
Day 4 – Classes form 09:30 – 17:30
Session 7 : Performance optimization

In this session we will learn about the various tools Python has for benchmarking code (i.e. measuring its memory and runtime performance) and for profiling code (identifying areas where improvements can be made). We will see that different tools are useful in different scenarios, and collect a set of recommendations for improving program performance. We will use these tools to illustrate and measure points about performance that have been made through the course. In the practical, we will take real-life code examples, measure their performance, and try to improve it. Core concepts introduced: function profiling, line profiling, profiler overhead, timing.

Session 8 : Unit testing

In this session we will begin with a gentle introduction to testing which will illustrate why it is useful and what type of problems it can solve. We will run through a series of examples using Python’s built in testing tools which will cover a number of different testing scenarios. We will then implement the same set of tests using the pytest testing framework and examine how using a framework makes the tests easier to write and interpret. After looking at a number of specialized tests for different types of code, we will discuss the impact of program design on testing. In the practical we will practise building and running test suites for existing code.

Dr. Martin Jones

Martin a freelance trainer specialising in teaching programming (mostly Python) and Linux skills to researchers in the field of biology. He trained as a biologist and completed his PhD in large-scale phylogenetics in 2007, then held a number of academic positions at the University of Edinburgh ending in a two year stint as Lecturer in Bioinformatics. I launched Python for Biologists in 2015 and have been teaching and writing full-time ever since.

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