PR Stats

Introduction to Generalised Linear Mixed Models for Ecologists
2025-12-08 - 2025-12-12 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
Standard modelling procedure for Species Distribution and Ecological Niche Modelling (SDMS01)

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

Multivariate Analysis of Ecological Communities Using VEGAN
2025-12-08 - 2025-12-12 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
Standard modelling procedure for Species Distribution and Ecological Niche Modelling (SDMS01)

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

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

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

Data Visualisation in R using ggplot2
2025-12-03 - 2025-12-05 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom

Bayesian Multilevel Modelling using brms for Ecologists (BMME02)
2025-12-01 - 2025-12-05 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom

Advanced Species Distribution Modelling (SDM's) and Ecological Niche Modelling (ENM's)
2025-12-01 - 2025-12-05 | Delivered remotely (Portugal), Portugal

Tree-Based Models
2025-12-01 - 2025-12-02 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom

Zero-Inflated Models (COURSE FULL)
2025-12-01 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom

Single cell RNA-Seq analysis (SCRN01)
2025-11-24 - 2025-11-27 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
Standard modelling procedure for Species Distribution and Ecological Niche Modelling (SDMS01)

Event Date
Tuesday, November 18th, 2025

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

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

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

Intended Audiences

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

Venue
Delivered remotely

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

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

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

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

Equipment and software requirements

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

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

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

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

Download R

Download RStudio

Download Zoom

Tickets

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SDMS01 ONLINE

SDMS01 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

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
Work Homepage
Personal Homepage

Analysis of Avian Point-Count Data in the Presence of Detection Error
2025-11-18 - 2025-11-20 | Delivered remotely (United Kingdom), Western European Time, United Kingdom

Introduction to Spatial Data Analysis (ISDA01)
2025-11-17 - 2025-11-21 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom

Spatial Data Visualisation and Mapping using TMAP
2025-11-10 - 2025-11-15 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
Standard modelling procedure for Species Distribution and Ecological Niche Modelling (SDMS01)

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.

SDMS01 ONLINE

SDMS01 ONLINE

£
275.00

Unlimited

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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

Network Analysis for Ecologists
2025-11-04 - 2025-11-05 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom

Model Selection and Model Simplification (COURSE FULL)
2025-11-03 - 2025-11-04 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom

Bayesian Multilevel Modelling using brms for Ecologists (COURSE FULL)
2025-10-20 - 2025-10-31 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom

Path Analysis, Structural Equations, and Causal Inference for Biologists
2025-10-20 - 2025-10-30 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
Standard modelling procedure for Species Distribution and Ecological Niche Modelling (SDMS01)

Event Date
Monday, October 20th, 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 – Quebec (Canada) 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, based primarily on my 2016 book, teaches you how to use path analysis and structural equations modelling to test causal hypotheses using observational data that is typical of research in ecology and evolution. It is taught in half-day sessions so that you can practice individually after each half-day session. You will learn how to conduct these tests, why (andwhen) they are justified, and how to interpret the results. The first few lectures will primarily present the theory but practical sessions will become more prominent later in the course. Thepractical work will be based on R and RStudio. Students will receive R script, datasets, and a list of R packages to install. It is highly recommended that each student have a copy of my 2016 book for the course, but not essential.

By the end of the course, participants should be able to: Understand the logical relationships between d-separation, data, and causal hypotheses. Know when to use piecewise SEM, when to use covariance- based SEM, and the advantages/disadvantages and assumptions of each Be able to construct, test, and interpret measurement models involving latent variables Be able to construct and identify equivalent models Be able to incorporate nested or mixed models, multigroup models, and non-normal distributions into SEM

Participants are encouraged to bring their own data, as there will be opportunities throughout the course to plan, analyze, and receive feedback on structural equation models.

Intended Audiences
Scientists generally, and ecologists specifically, who want to test hypotheses concerning cause-and-effect relationships involving several variables, especially involving observational data.

Venue
Delivered remotely

Course Details
Time Zone – Quebec (Canada) local time
Availability – TBC
Duration – 9 days, 4 hours per day
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
This course involves a mixture of theory and practical work. Data and analytical approaches will be presented in a lecture format to explain key concepts. Statistical analyses will then be presented using R. All R script that the instructor uses during these sessions will be shared with participants, and R script will be presented and explained.

Assumed quantative knowledge

Experience in using R and RStudio for statistical analysis.
A basic understanding of statistical inference and regression methods.
A familiarity of more advanced regression models (mixed models, generalized linear models) is an asset but is not essential.

Assumed computer background
Proficiency with R programming language, including: importing/exporting data; manipulating data in the R environment; constructing and evaluating basic statistical models (e.g., lm()).

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 20th
Day 1 – Classes from 08:30 – 12:30
Causal inference using experiments vs. observations (4h)

Randomised experiments are the gold standard
Limitations on randomised experiments
The logic of controlled experiments
Limitations of controlled experiments
Physical control vs. observational control DAGs, d-separation and data (2h)\nTranslating from the language of causality to the language of statistics
Directed acyclic graphs (DAGS) and d-separation
D-separation and statistical conditioning
The difference between experimental control and statistical conditioning
The logic of causal inference using d-separation

Tuesday 21st
Day 2 – Classes from 08:30 – 12:30
Path analysis using piecewise structural equation modelling (1h30)

D-separation basis sets of a DAG
The steps in conducting a piecewise SEM
Rejecting or provisionally accepting your path model
Path coefficients as measures of direct causal effect
Decomposing causal effects

Practical work (2h)

Wednesday 22nd
Day 3 – Classes from 08:30 – 12:30
Path analysis using piecewiseSEM (2h30)

The piecewiseSEM library in R

Practical work (1h)

Thursday 23rd
Day 4 – Classes from 08:30 – 12:30
Equivalent models and AIC statistics (2h)

Statistical power in SEM
Provisionally accepting a causal hypothesis
What is a “d-separation equivalent” DAG
Rules for identifying equivalent models
AIC statistic to compare between non-equivalent models
How to interpret AIC statistics

Practical work (1h30)

Friday 24th
Day 5 – Classes from 08:30 – 12:30
Covariance-based path analysis (2h)

Translating the DAG into “structural equations”
The model-predicted covariance matrix
An intuitive explanation of maximum likelihood estimating
Estimating the free parameters via ML
The concept of “degrees of freedom”
The ML chi-squared statistic of model fit
Rejecting (or not) your SE model

Covariance-based path analysis using lavaan (1h30)

Monday 27th
Day 6 – Classes from 08:30 – 12:30
Latent variables and measurement models (3h)

Removing latent variables from a DAG
DAGs and MAGs
DAG.to.MAG() function
When you can’t remove a latent: measurement models
Measurement models and ML estimation
Fixing the scale of a latent variable
Measurement models and minimum degrees of freedom
Measurement models in lavaan
Empirical example: measuring soil fertility

Tuesday 28th
Day 7 – Classes from 08:30 – 12:30
Practical using measurement models (1h)
The full structural equation model (2h30)

Model identification: structural and empirical
Composite variables and composite latents
Consequences and solutions for small sample sizes
Consequences and solutions for non-normal data
Measures of approximate fit
Missing data
Reporting results in publications

Wednesday 29th
Day 8 – Classes from 08:30 – 12:30
Multigroup models (2h)

What is causal heterogeneity?
The concept of nested models
How to fit multigroup models in lavaan

Practical: putting everything together (1h30)

Thursday 30th
Day 9 – Classes from 08:30 – 12:30
Practical and group presentations of results

Bill Shipley

Bill Shipley is an experienced researcher and teacher in plant ecology and statistical ecology. He has published four scientific monographs and over 170 peer-reviewed papers.

Introduction to Machine Learning
2025-10-13 - 2025-10-17 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom

Introduction to Snakemake
2025-10-06 - 2025-10-17 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom

Bioacoustics Data Analysis
2025-10-06 - 2025-10-10 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
Standard modelling procedure for Species Distribution and Ecological Niche Modelling (SDMS01)

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 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

Spatial and Spatial-Temporal Modelling Using R-INLA
2025-09-22 - 2025-09-26 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
Standard modelling procedure for Species Distribution and Ecological Niche Modelling (SDMS01)

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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SDMS01 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 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

Species Distribution Modelling (SDMs) and Ecological Niche Modelling (ENMs) (SDMR06)
2025-09-22 - 2025-09-26 | Delivered remotely (Portugal), Portugal
Standard modelling procedure for Species Distribution and Ecological Niche Modelling (SDMS01)

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 websiteWork WebpageResearchGateGoogleScholar

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)
