BEGIN:VCALENDAR VERSION:2.0 PRODID:-//PR Stats - ECPv6.15.20//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:PR Stats X-ORIGINAL-URL:https://prstats.org X-WR-CALDESC:Events for PR Stats REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:Europe/London BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20250330T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20251026T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20260329T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20261025T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20270328T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20271031T010000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;VALUE=DATE:20260413 DTEND;VALUE=DATE:20260418 DTSTAMP:20260417T101009 CREATED:20260126T121108Z LAST-MODIFIED:20260127T163252Z UID:10000582-1776038400-1776470399@prstats.org SUMMARY:Machine Learning for Ecological Time Series (METR01) DESCRIPTION:Joint Species Distribution Modelling (JSDM) using HMSC: A Hierarchical Modelling Approach (JSDM01)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, September 22nd\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis 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. \nCourse Program\nTIME 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. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.\n \n \n \n \n \n \n \n \n \n \n Course Details\n 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. \nBy the end of the course participants should be able to: \n\nKnow the different types of spatial and spatio-temporal data available and how to work with them in R.\nKnow the different modelling approaches for spatial and spatio-temporal data.\nKnow how to visualize and produce maps of spatial and spatio-temporal data.\nBe able to fit models with the R-INLA package.\nKnow how to interpret the output from model fitting.\nBe confident with the use of INLA for data analysis.\nUnderstand the different models that can be fit with INLA to spatial and spatio-temporal data.\nKnow how to define the different parts of a model with INLA.\nHave the confidence to use INLA for their own projects.\n\n \n \n \n \n \n Intended Audiences\n \nAcademics 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.\nApplied 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.\nThe 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.\n\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Information\n Time zone – Spain (GMT+2) local time \nAvailability – 20 places \nDuration – 5 days \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n  \nPLEASE 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.\n \n \n \n \n \n Teaching Format\n \n\nThe 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. \n\n\n \n \n \n \n \n Assumed quantitative knowledge\n 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.). \n \n \n \n \n \n \n Assumed computer background\n 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.\n \n \n \n \n \n Equipment and software requirements\n \n\nA 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/. \n\n\nAll 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. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n\n\n  \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n 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. \n\n\n\n \n \n HMSC01 ONLINE\n \n HMSC01 ONLINE\n\n \n \n \n \n £\n 450.00\n \n \n\n \n Unlimited \n \n \n Open the ticket description.\n More \n \n Close the ticket description.\n Less \n \n \n\n \n \n Decrease ticket quantity for HMSC01 ONLINE\n -\n \n \n Quantity \n \n\n \n Increase ticket quantity for HMSC01 ONLINE\n +\n \n \n \n \n\n \n \n Quantity: \n 0\n\n \n \n Total: \n \n \n \n £\n 0.00\n \n \n\n \n Get Tickets\n \n\n \n \n \n\n \n \n\n \n \n\n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations 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.\n \n \n \n \n \n If you are unsure about course suitability\, please get in touch by email to find out more \noliverhooker@prstatistics.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Monday 22nd\n Day 1 – Classes from 14:00 to 21:00 \n\nSession 1 – Intro to INLA\nPractical 1 – Intro to INLA\nSession 2 – Model fitting with INLA\nPractical 2 – Model fitting with INLA\nSession 3 – GLMM’s with INLA\nPractical 3 – GLMM’s with INLA\nQ and A and end of day summary\n\n \n \n \n \n \n Tuesday 23rd\n Day 2 – Classes from 14:00 to 21:00 \n\nSession 4 – Spatial Data\nPractical 4 – Spatial Data\nSession 5 – Spatio-Temporal Data\nPractical 5 – Spatio-Temporal Data\nSession 6 – Advanced Visualisation\nPractical 6 – Advanced Visualisation\nQ and A and end of day summary\n\n \n \n \n \n \n Wednesday 24th\n Day 3 – Classes from 14:00 to 21:00 \n\nSession 7 – Spatial Models for Lattice Data\nPractical 7 – Spatial Models for Lattice Data\nSession 8 – Spatial Models for Continuous Data\nPractical 8 – Spatial Models for Continuous Data\nSession 9 – Spatial Models for Point Patterns\nPractical 9 – Spatial Models for Point Patterns\nQ and A and end of day summary\n\n \n \n \n \n \n Thursday 25th\n Day 4 – Classes from 14:00 to 21:00 \n\nSession 10 – Spatio-Temporal Models for Lattice Data\nPractical 10 – Spatio-Temporal Models for Lattice Data\nSession 11 – Spatio-Temporal Models  for Continuous Data\nPractical 11 – Spatio-Temporal Models  for Continuous Data\nSession 12 – Spatio-Temporal Models  for Point Patterns\nPractical 12 – Spatio-Temporal Models  for Point Patterns\nQ and A and end of day summary\n\n \n \n \n \n \n Friday 26th\n Day 5 – Classes from 14:00 to 21:00 \n\nCase studies\, own data and problem solving.\n\n \n \n \n \n \n \n \n \n Dr Virgillio Gomez Rubio\n \n 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\n\n\nResearchgate\n\nGoogle Scholar\n\nORCID\n\nGitHub URL:https://prstats.org/course/machine-learning-for-ecological-time-series-metr01/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.org/wp-content/uploads/2026/01/METR01.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260420 DTEND;VALUE=DATE:20260424 DTSTAMP:20260417T101010 CREATED:20251127T171752Z LAST-MODIFIED:20260408T130542Z UID:10000566-1776643200-1776988799@prstats.org SUMMARY:Analysing Ecological Data with Detection Error (AEDD01) DESCRIPTION:Joint Species Distribution Modelling (JSDM) using HMSC: A Hierarchical Modelling Approach (JSDM01)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, November 18th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis 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. \nTime Zone\nTIME ZONE – UK (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n About this course\n 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. \nSuch 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. \nAnalysis 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. \nThe 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. \nThe expected outcome of the course is a solid foundation for further professional development via increased confidence in applying these methods for field observations. \nBy the end of the course\, participants should be able to: \n\nUnderstand basic statistical concepts related to detection error\nWork with field collected data and data from automated recording units (ARU)\nKnow packages such as unmarked\, detect\, bSims\nCritically evaluate modelling options and assumptions using simulations\nFit N-mixture\, distance sampling\, and time-removal models to data\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to avian data\nApplied 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\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – UK (GMT) local time \nAvailability – 25 places \nDuration – 3 days\, 4 hours per day \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT \nLanguage – English \n \n \n \n \n \n Teaching Format\n 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. \n \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical\, mathematical and physical concepts. Specifically\, generalised linear regression models\, including mixed models; basic knowledge of calculus. \n \n \n \n \n \n Assumed computer background\n 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. \n \n \n \n \n \n Equipment and software requirements\n \nA 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/. \n\n\nAll 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. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n 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. \n\n\n\n \n \n HMSC01 ONLINE\n \n HMSC01 ONLINE\n\n \n \n \n \n £\n 450.00\n \n \n\n \n Unlimited \n \n \n Open the ticket description.\n More \n \n Close the ticket description.\n Less \n \n \n\n \n \n Decrease ticket quantity for HMSC01 ONLINE\n -\n \n \n Quantity \n \n\n \n Increase ticket quantity for HMSC01 ONLINE\n +\n \n \n \n \n\n \n \n Quantity: \n 0\n\n \n \n Total: \n \n \n \n £\n 0.00\n \n \n\n \n Get Tickets\n \n\n \n \n \n\n \n \n\n \n \n\n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations 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.\n \n \n \n \n \n \n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Tuesday 18th\n Day 1 – Classes from 13:30 – 17:30 \nIntroduction \n\nIntroduction and background\nReview of field sampling techniques\nIntroduction to agent-based simulations\nOverview of regression techniques\nNaïve estimates of occupancy and abundance\nMultiple visits and N-mixture models\n\n \n \n \n \n \n Wednesday 19th\n Day 2 – Classes from 13:30 – 17:30 \nIntroduction to modelling \n\nBird behaviour\nTime-removal models\nObservation process\nDistance sampling\nCombining removal and distance sampling (QPAD)\n\n \n \n \n \n \n Thursday 20th\n Day 3 – Classes from 13:30 – 17:30 \nDifferent approaches \n\nSingle visit-based approaches (N-mixture and SQPAD)\nAnalysing data from recording units\nMulti-species models and using species traits and phylogeny\nDealing with roadside and other biases\nClosing remarks\n\n \n \n \n \n \n \n Course Instructor\n \nDr. Peter Solymos \nPéter 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. \nGoogle Scholar \nWork Homepage \nPersonal Homepage URL:https://prstats.org/course/analysing-ecological-data-with-detection-error-aedd01/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2025/04/APCD01-1.jpg GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260421 DTEND;VALUE=DATE:20260423 DTSTAMP:20260417T101010 CREATED:20251202T213514Z LAST-MODIFIED:20260311T123815Z UID:10000569-1776729600-1776902399@prstats.org SUMMARY:Deep Learning using R (DLUR01) DESCRIPTION:Joint Species Distribution Modelling (JSDM) using HMSC: A Hierarchical Modelling Approach (JSDM01)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, September 22nd\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis 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. \nCourse Program\nTIME 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. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n Course Details\n 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. \nBy the end of the course participants should be able to: \n\nKnow the different types of spatial and spatio-temporal data available and how to work with them in R.\nKnow the different modelling approaches for spatial and spatio-temporal data.\nKnow how to visualize and produce maps of spatial and spatio-temporal data.\nBe able to fit models with the R-INLA package.\nKnow how to interpret the output from model fitting.\nBe confident with the use of INLA for data analysis.\nUnderstand the different models that can be fit with INLA to spatial and spatio-temporal data.\nKnow how to define the different parts of a model with INLA.\nHave the confidence to use INLA for their own projects.\n\n \n \n \n \n \n Intended Audiences\n \nAcademics 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.\nApplied 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.\nThe 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.\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Information\n Time zone – Spain (GMT+2) local time \nAvailability – 20 places \nDuration – 5 days \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n  \nPLEASE 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. \n \n \n \n \n \n Teaching Format\n \n\nThe 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. \n\n\n \n \n \n \n \n Assumed quantitative knowledge\n 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.). \n  \n \n \n \n \n \n Assumed computer background\n 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. \n \n \n \n \n \n Equipment and software requirements\n \n\nA 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/. \n\n\nAll 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. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n\n\n  \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n 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. \n\n\n\n \n \n HMSC01 ONLINE\n \n HMSC01 ONLINE\n\n \n \n \n \n £\n 450.00\n \n \n\n \n Unlimited \n \n \n Open the ticket description.\n More \n \n Close the ticket description.\n Less \n \n \n\n \n \n Decrease ticket quantity for HMSC01 ONLINE\n -\n \n \n Quantity \n \n\n \n Increase ticket quantity for HMSC01 ONLINE\n +\n \n \n \n \n\n \n \n Quantity: \n 0\n\n \n \n Total: \n \n \n \n £\n 0.00\n \n \n\n \n Get Tickets\n \n\n \n \n \n\n \n \n\n \n \n\n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations 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. \n \n \n \n \n \n If you are unsure about course suitability\, please get in touch by email to find out more \noliverhooker@prstatistics.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Monday 22nd\n Day 1 – Classes from 14:00 to 21:00 \n\nSession 1 – Intro to INLA\nPractical 1 – Intro to INLA\nSession 2 – Model fitting with INLA\nPractical 2 – Model fitting with INLA\nSession 3 – GLMM’s with INLA\nPractical 3 – GLMM’s with INLA\nQ and A and end of day summary\n\n \n \n \n \n \n Tuesday 23rd\n Day 2 – Classes from 14:00 to 21:00 \n\nSession 4 – Spatial Data\nPractical 4 – Spatial Data\nSession 5 – Spatio-Temporal Data\nPractical 5 – Spatio-Temporal Data\nSession 6 – Advanced Visualisation\nPractical 6 – Advanced Visualisation\nQ and A and end of day summary\n\n \n \n \n \n \n Wednesday 24th\n Day 3 – Classes from 14:00 to 21:00 \n\nSession 7 – Spatial Models for Lattice Data\nPractical 7 – Spatial Models for Lattice Data\nSession 8 – Spatial Models for Continuous Data\nPractical 8 – Spatial Models for Continuous Data\nSession 9 – Spatial Models for Point Patterns\nPractical 9 – Spatial Models for Point Patterns\nQ and A and end of day summary\n\n \n \n \n \n \n Thursday 25th\n Day 4 – Classes from 14:00 to 21:00 \n\nSession 10 – Spatio-Temporal Models for Lattice Data\nPractical 10 – Spatio-Temporal Models for Lattice Data\nSession 11 – Spatio-Temporal Models  for Continuous Data\nPractical 11 – Spatio-Temporal Models  for Continuous Data\nSession 12 – Spatio-Temporal Models  for Point Patterns\nPractical 12 – Spatio-Temporal Models  for Point Patterns\nQ and A and end of day summary\n\n \n \n \n \n \n Friday 26th\n Day 5 – Classes from 14:00 to 21:00 \n\nCase studies\, own data and problem solving.\n\n \n \n \n \n \n \n \n \n Dr Virgillio Gomez Rubio\n \n 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\n \n\nResearchgate\n \nGoogle Scholar\n \nORCID\n \nGitHub URL:https://prstats.org/course/deep-learning-using-r-dlur01/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.org/wp-content/uploads/2025/12/DLUR01.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/London:20260423T000000 DTEND;TZID=Europe/London:20260424T235900 DTSTAMP:20260417T101010 CREATED:20260129T174438Z LAST-MODIFIED:20260416T144307Z UID:10000586-1776902400-1777075140@prstats.org SUMMARY:Standard modelling procedure for Species Distribution and Ecological Niche Modelling (SDMS01) DESCRIPTION:Joint Species Distribution Modelling (JSDM) using HMSC: A Hierarchical Modelling Approach (JSDM01)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, September 22nd\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis 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. \nTime Zone\nTIME ZONE – Portugal (GMT+1) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).\n \n \n \n \n \n \n \n \n \n \n About This Course\n 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. \nBy the end of the course\, participants should be able to: \n\nCalculate ecological niche models and specie distribution models\nUnderstand their results\, as well as to choose and apply the correct.\nHow to choose the best methodology depending on the aim of their type of study and data.\n\n  \n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to spatial data.\nStudents and researchers working on biogeography\, spatial ecology\, or related disciplines with experience in ecological niche models.\n\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Details\n Time Zone – Portugal (GMT+1) local time \nAvailability – 30 Places \nDuration – 5 days\, 7 hours a day \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English\n \n \n \n \n \n Teaching Format\n 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.\n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical and ecological concepts.\n \n \n \n \n \n Assumed computer background\n 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).\n \n \n \n \n \n Equipment and software requirements\n \nA 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/. \n\n\nAll 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. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n 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. \n\n\n\n \n \n HMSC01 ONLINE\n \n HMSC01 ONLINE\n\n \n \n \n \n £\n 450.00\n \n \n\n \n Unlimited \n \n \n Open the ticket description.\n More \n \n Close the ticket description.\n Less \n \n \n\n \n \n Decrease ticket quantity for HMSC01 ONLINE\n -\n \n \n Quantity \n \n\n \n Increase ticket quantity for HMSC01 ONLINE\n +\n \n \n \n \n\n \n \n Quantity: \n 0\n\n \n \n Total: \n \n \n \n £\n 0.00\n \n \n\n \n Get Tickets\n \n\n \n \n \n\n \n \n\n \n \n\n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations 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.\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Monday 22nd\n Day 1 – Classes from 09:30 to 17:30 \n\nTopic 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.\nTopic 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.\nTopic 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.\n\n \n \n \n \n \n Tuesday 23rd\n Day 2 – Classes from 09:30 to 17:30 \n\nTopic 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.\nTopic 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.\n\n \n \n \n \n \n \n Wednesday 24th\n Day 3 – Classes from 09:30 to 17:30 \n\nTopic 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.\nTopic 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.\n\n \n \n \n \n \n Thursday 25th\n Day 4 – Classes from 09:30 to 17:30 \n\nTopic 8: Modelling with Maxent. Formatting the data\, parameterising Maxent\, calculating the models\, evaluating the models\, projecting the models over time and space.\nTopic 9: Compare statistically two different ecological niche models using the R package ecospat.\n\n \n \n \n \n \n \n Friday 26th\n Day 5 – Classes from 09:30 to 17:30 \n\nTopic 10: Run ecological niche models with your own data.\nTopic 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.\n\n \n \n \n \n \n \n \n \n \n Dr. Neftali Sillero\n \n 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. \nHe 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 \nPersonal website\nWork Webpage\nResearchGate\nGoogleScholar\n \n \n \n \n \n \n \n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM) URL:https://prstats.org/course/standard-modelling-procedure-for-species-distribution-and-ecological-niche-modelling-sdms01/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2026/01/SDMS01-1.jpg GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/London:20260427T000000 DTEND;TZID=Europe/London:20260430T235900 DTSTAMP:20260417T101010 CREATED:20260129T115223Z LAST-MODIFIED:20260311T181917Z UID:10000585-1777248000-1777593540@prstats.org SUMMARY:Stable Isotope Mixing Models Using SIBER\, SIAR\, MixSIAR (SIMM12) DESCRIPTION:Joint Species Distribution Modelling (JSDM) using HMSC: A Hierarchical Modelling Approach (JSDM01)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, September 22nd\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis 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. \nTime Zone\nTIME ZONE – Portugal (GMT+1) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).\n \n \n \n \n \n \n \n \n \n \n About This Course\n 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. \nBy the end of the course\, participants should be able to: \n\nCalculate ecological niche models and specie distribution models\nUnderstand their results\, as well as to choose and apply the correct.\nHow to choose the best methodology depending on the aim of their type of study and data.\n\n  \n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to spatial data.\nStudents and researchers working on biogeography\, spatial ecology\, or related disciplines with experience in ecological niche models.\n\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Details\n Time Zone – Portugal (GMT+1) local time \nAvailability – 30 Places \nDuration – 5 days\, 7 hours a day \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English\n \n \n \n \n \n Teaching Format\n 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.\n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical and ecological concepts.\n \n \n \n \n \n Assumed computer background\n 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).\n \n \n \n \n \n Equipment and software requirements\n \nA 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/. \n\n\nAll 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. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n 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. \n\n\n\n \n \n HMSC01 ONLINE\n \n HMSC01 ONLINE\n\n \n \n \n \n £\n 450.00\n \n \n\n \n Unlimited \n \n \n Open the ticket description.\n More \n \n Close the ticket description.\n Less \n \n \n\n \n \n Decrease ticket quantity for HMSC01 ONLINE\n -\n \n \n Quantity \n \n\n \n Increase ticket quantity for HMSC01 ONLINE\n +\n \n \n \n \n\n \n \n Quantity: \n 0\n\n \n \n Total: \n \n \n \n £\n 0.00\n \n \n\n \n Get Tickets\n \n\n \n \n \n\n \n \n\n \n \n\n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations 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.\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Monday 22nd\n Day 1 – Classes from 09:30 to 17:30 \n\nTopic 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.\nTopic 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.\nTopic 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.\n\n \n \n \n \n \n Tuesday 23rd\n Day 2 – Classes from 09:30 to 17:30 \n\nTopic 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.\nTopic 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.\n\n \n \n \n \n \n Wednesday 24th\n Day 3 – Classes from 09:30 to 17:30 \n\nTopic 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.\nTopic 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.\n\n \n \n \n \n \n Thursday 25th\n Day 4 – Classes from 09:30 to 17:30 \n\nTopic 8: Modelling with Maxent. Formatting the data\, parameterising Maxent\, calculating the models\, evaluating the models\, projecting the models over time and space.\nTopic 9: Compare statistically two different ecological niche models using the R package ecospat.\n\n \n \n \n \n \n Friday 26th\n Day 5 – Classes from 09:30 to 17:30 \n\nTopic 10: Run ecological niche models with your own data.\nTopic 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.\n\n \n \n \n \n \n \n \n \n Dr. Neftali Sillero\n \n 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. \nHe 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 \nPersonal website\nWork Webpage\nResearchGate\nGoogleScholar\n \n \n \n \n \n \n \n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM) URL:https://prstats.org/course/stable-isotope-mixing-models-using-siber-siar-mixsiar-simm12/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2025/09/SIMMPR-1.jpg GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260427 DTEND;VALUE=DATE:20260502 DTSTAMP:20260417T101010 CREATED:20260114T115043Z LAST-MODIFIED:20260413T143539Z UID:10000579-1777248000-1777679999@prstats.org SUMMARY:Bayesian Nonlinear Models for Ecologists (BNLM01) DESCRIPTION:Joint Species Distribution Modelling (JSDM) using HMSC: A Hierarchical Modelling Approach (JSDM01)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, December 1st\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis 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. \nTime Zone\nTIME ZONE – Portugal (GMT+1) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).\n \n \n \n \n \n \n \n \n \n \n About This Course\n 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”. \nEcological 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. \nBy the end of 5-day practical course should be able to: \n\nfilter records and select your variables with variance inflation factor;\ntest the effect of Maxent regularization parameter in models performance;\nvalidate models performance and accuracy;\nperform MESS analysis\, null models\, and mechanistic models\, as well as to build your “virtual species”.\n\nStudents will learn to use functions implemented in the packages “usdm”; “dismo”; “ENMEval”; “SDMvspecies”; “spThin”; and “NicheMapper” among others.\n \n \n \n \n \n Intended Audiences\n 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.\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Details\n Time Zone – Portugal (GMT+!) local time \nAvailability – 24 places \nDuration – 5 days\, 7 hours a day \nContact hours – Approx. 35 hours \nECT’s – Equal to 3ECT’s \nLanguage – English\n \n \n \n \n \n Teaching Format\n 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.\n \n \n \n \n \n Assumed quantitative knowledge\n 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.\n \n \n \n \n \n Assumed computer background\n 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).\n \n \n \n \n \n Equipment and software requirements\n \nA 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/. \n\n\nAll 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. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n 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. \n\n\n\n \n \n HMSC01 ONLINE\n \n HMSC01 ONLINE\n\n \n \n \n \n £\n 450.00\n \n \n\n \n Unlimited \n \n \n Open the ticket description.\n More \n \n Close the ticket description.\n Less \n \n \n\n \n \n Decrease ticket quantity for HMSC01 ONLINE\n -\n \n \n Quantity \n \n\n \n Increase ticket quantity for HMSC01 ONLINE\n +\n \n \n \n \n\n \n \n Quantity: \n 0\n\n \n \n Total: \n \n \n \n £\n 0.00\n \n \n\n \n Get Tickets\n \n\n \n \n \n\n \n \n\n \n \n\n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations 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.\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Monday 1st\n Day 1 – Classes from 09:30 to 17:30 \n\nENM guide: how to model\nENM R packages.\nSources of environmental variables using geodata package.\nGetting species records with geodata package.\n\n \n \n \n \n \n Tuesday 2nd\n Day 2 – Classes from 09:30 to 17:30 \n\nVariable selection with variance inflation factor (VIF) and usdm packages.\nChoosing the correct study area.\nFiltering records using usdm/spThin packages.\nChoosing pseudo-absences with Biomod2 package.\n\n \n \n \n \n \n Wednesday 3rd\n Day 3 – Classes from 09:30 to 17:30 \n\nSplit records in training and test with ENMeval package.\nTest effect of Maxent regularization parameter.<.li>\nComparing correlative models with AIC\, with ENMeval package.\n\n \n \n \n \n \n Thursday 4th\n Day 4 – Classes from 09:30 to 17:30 \n\nMESS practice with Biomod2 package.\nValidate models null models.\nVirtualSpecies virtualspecies packages.\n\n \n \n \n \n \n Friday 5th\n Day 5 – Classes from 09:30 to 17:30 \n\nMechanistic model NicheMapper packages.\n\n \n \n \n \n \n \n \n \n Dr. Neftali Sillero\n \n 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. \nHe 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 \nPersonal website \nWork Webpage \nResearchGate \nGoogleScholar\n \n \n \n \n \n \n \n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n \n \n \n \n \n \n \n \n Dr. Salvador Arenas-Castro\n \n Dr. Salvador Arenas-Castro is a broad-spectrum ecologist with interesting in different\nintegrative perspective of the fundamental ecology\, macroecology and biogeography\nwith their both application and relationship to climate and land management. He is also\nexploring other research sources in agroecology\, forestry\, spatial ecology\, and\necoinformatics\, all addressed by explicitly considering the spatial component of\necological processes\, mainly applying spatially explicit modelling approaches\, GIS and\nremote sensing techniques. Please check his webpage for further information:\nhttps://salvadorarenascastro.wordpress.com \nGoogle Scholar: https://scholar.google.com/citations?user=UAYiB5UAAAAJ&hl=es&oi=ao\nResearchGate: https://www.researchgate.net/profile/Salvador-Arenas-Castro URL:https://prstats.org/course/bayesian-nonlinear-models-for-ecologists-bnlm01/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:Home Courses,Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2026/01/BNLM01.jpg GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260428 DTEND;VALUE=DATE:20260430 DTSTAMP:20260417T101010 CREATED:20260106T141258Z LAST-MODIFIED:20260110T095824Z UID:10000573-1777334400-1777507199@prstats.org SUMMARY:Interactive Data Applications with Shiny (SHID01) DESCRIPTION:Joint Species Distribution Modelling (JSDM) using HMSC: A Hierarchical Modelling Approach (JSDM01)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, September 22nd\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis 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. \nCourse Program\nTIME 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. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.\n \n \n \n \n \n \n \n \n \n \n Course Details\n 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. \nBy the end of the course participants should be able to: \n\nKnow the different types of spatial and spatio-temporal data available and how to work with them in R.\nKnow the different modelling approaches for spatial and spatio-temporal data.\nKnow how to visualize and produce maps of spatial and spatio-temporal data.\nBe able to fit models with the R-INLA package.\nKnow how to interpret the output from model fitting.\nBe confident with the use of INLA for data analysis.\nUnderstand the different models that can be fit with INLA to spatial and spatio-temporal data.\nKnow how to define the different parts of a model with INLA.\nHave the confidence to use INLA for their own projects.\n\n \n \n \n \n \n Intended Audiences\n \nAcademics 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.\nApplied 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.\nThe 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.\n\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Information\n Time zone – Spain (GMT+2) local time \nAvailability – 20 places \nDuration – 5 days \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n  \nPLEASE 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.\n \n \n \n \n \n Teaching Format\n \n\nThe 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. \n\n\n \n \n \n \n \n Assumed quantitative knowledge\n 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.). \n \n \n \n \n \n \n Assumed computer background\n 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.\n \n \n \n \n \n Equipment and software requirements\n \n\nA 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/. \n\n\nAll 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. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n\n\n  \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n 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. \n\n\n\n \n \n HMSC01 ONLINE\n \n HMSC01 ONLINE\n\n \n \n \n \n £\n 450.00\n \n \n\n \n Unlimited \n \n \n Open the ticket description.\n More \n \n Close the ticket description.\n Less \n \n \n\n \n \n Decrease ticket quantity for HMSC01 ONLINE\n -\n \n \n Quantity \n \n\n \n Increase ticket quantity for HMSC01 ONLINE\n +\n \n \n \n \n\n \n \n Quantity: \n 0\n\n \n \n Total: \n \n \n \n £\n 0.00\n \n \n\n \n Get Tickets\n \n\n \n \n \n\n \n \n\n \n \n\n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations 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.\n \n \n \n \n \n If you are unsure about course suitability\, please get in touch by email to find out more \noliverhooker@prstatistics.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Monday 22nd\n Day 1 – Classes from 14:00 to 21:00 \n\nSession 1 – Intro to INLA\nPractical 1 – Intro to INLA\nSession 2 – Model fitting with INLA\nPractical 2 – Model fitting with INLA\nSession 3 – GLMM’s with INLA\nPractical 3 – GLMM’s with INLA\nQ and A and end of day summary\n\n \n \n \n \n \n Tuesday 23rd\n Day 2 – Classes from 14:00 to 21:00 \n\nSession 4 – Spatial Data\nPractical 4 – Spatial Data\nSession 5 – Spatio-Temporal Data\nPractical 5 – Spatio-Temporal Data\nSession 6 – Advanced Visualisation\nPractical 6 – Advanced Visualisation\nQ and A and end of day summary\n\n \n \n \n \n \n Wednesday 24th\n Day 3 – Classes from 14:00 to 21:00 \n\nSession 7 – Spatial Models for Lattice Data\nPractical 7 – Spatial Models for Lattice Data\nSession 8 – Spatial Models for Continuous Data\nPractical 8 – Spatial Models for Continuous Data\nSession 9 – Spatial Models for Point Patterns\nPractical 9 – Spatial Models for Point Patterns\nQ and A and end of day summary\n\n \n \n \n \n \n Thursday 25th\n Day 4 – Classes from 14:00 to 21:00 \n\nSession 10 – Spatio-Temporal Models for Lattice Data\nPractical 10 – Spatio-Temporal Models for Lattice Data\nSession 11 – Spatio-Temporal Models  for Continuous Data\nPractical 11 – Spatio-Temporal Models  for Continuous Data\nSession 12 – Spatio-Temporal Models  for Point Patterns\nPractical 12 – Spatio-Temporal Models  for Point Patterns\nQ and A and end of day summary\n\n \n \n \n \n \n Friday 26th\n Day 5 – Classes from 14:00 to 21:00 \n\nCase studies\, own data and problem solving.\n\n \n \n \n \n \n \n \n \n Dr Virgillio Gomez Rubio\n \n 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\n\n\nResearchgate\n\nGoogle Scholar\n\nORCID\n\nGitHub URL:https://prstats.org/course/interactive-data-applications-with-shiny-shid01/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2026/01/SHID01-1.jpg GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260504 DTEND;VALUE=DATE:20260509 DTSTAMP:20260417T101010 CREATED:20260219T144931Z LAST-MODIFIED:20260219T175452Z UID:10000590-1777852800-1778284799@prstats.org SUMMARY:Bayesian Modelling Using R-INLA Course (BMIN04) DESCRIPTION:Joint Species Distribution Modelling (JSDM) using HMSC: A Hierarchical Modelling Approach (JSDM01)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, September 22nd\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis 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. \nCourse Program\nTIME 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. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.\n \n \n \n \n \n \n \n \n \n \n Course Details\n 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. \nBy the end of the course participants should be able to: \n\nKnow the different types of spatial and spatio-temporal data available and how to work with them in R.\nKnow the different modelling approaches for spatial and spatio-temporal data.\nKnow how to visualize and produce maps of spatial and spatio-temporal data.\nBe able to fit models with the R-INLA package.\nKnow how to interpret the output from model fitting.\nBe confident with the use of INLA for data analysis.\nUnderstand the different models that can be fit with INLA to spatial and spatio-temporal data.\nKnow how to define the different parts of a model with INLA.\nHave the confidence to use INLA for their own projects.\n\n \n \n \n \n \n Intended Audiences\n \nAcademics 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.\nApplied 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.\nThe 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.\n\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Information\n Time zone – Spain (GMT+2) local time \nAvailability – 20 places \nDuration – 5 days \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n  \nPLEASE 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.\n \n \n \n \n \n Teaching Format\n \n\nThe 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. \n\n\n \n \n \n \n \n Assumed quantitative knowledge\n 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.). \n \n \n \n \n \n \n Assumed computer background\n 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.\n \n \n \n \n \n Equipment and software requirements\n \n\nA 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/. \n\n\nAll 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. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n\n\n  \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n 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. \n\n\n\n \n \n HMSC01 ONLINE\n \n HMSC01 ONLINE\n\n \n \n \n \n £\n 450.00\n \n \n\n \n Unlimited \n \n \n Open the ticket description.\n More \n \n Close the ticket description.\n Less \n \n \n\n \n \n Decrease ticket quantity for HMSC01 ONLINE\n -\n \n \n Quantity \n \n\n \n Increase ticket quantity for HMSC01 ONLINE\n +\n \n \n \n \n\n \n \n Quantity: \n 0\n\n \n \n Total: \n \n \n \n £\n 0.00\n \n \n\n \n Get Tickets\n \n\n \n \n \n\n \n \n\n \n \n\n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations 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.\n \n \n \n \n \n If you are unsure about course suitability\, please get in touch by email to find out more \noliverhooker@prstatistics.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Monday 22nd\n Day 1 – Classes from 14:00 to 21:00 \n\nSession 1 – Intro to INLA\nPractical 1 – Intro to INLA\nSession 2 – Model fitting with INLA\nPractical 2 – Model fitting with INLA\nSession 3 – GLMM’s with INLA\nPractical 3 – GLMM’s with INLA\nQ and A and end of day summary\n\n \n \n \n \n \n Tuesday 23rd\n Day 2 – Classes from 14:00 to 21:00 \n\nSession 4 – Spatial Data\nPractical 4 – Spatial Data\nSession 5 – Spatio-Temporal Data\nPractical 5 – Spatio-Temporal Data\nSession 6 – Advanced Visualisation\nPractical 6 – Advanced Visualisation\nQ and A and end of day summary\n\n \n \n \n \n \n Wednesday 24th\n Day 3 – Classes from 14:00 to 21:00 \n\nSession 7 – Spatial Models for Lattice Data\nPractical 7 – Spatial Models for Lattice Data\nSession 8 – Spatial Models for Continuous Data\nPractical 8 – Spatial Models for Continuous Data\nSession 9 – Spatial Models for Point Patterns\nPractical 9 – Spatial Models for Point Patterns\nQ and A and end of day summary\n\n \n \n \n \n \n Thursday 25th\n Day 4 – Classes from 14:00 to 21:00 \n\nSession 10 – Spatio-Temporal Models for Lattice Data\nPractical 10 – Spatio-Temporal Models for Lattice Data\nSession 11 – Spatio-Temporal Models  for Continuous Data\nPractical 11 – Spatio-Temporal Models  for Continuous Data\nSession 12 – Spatio-Temporal Models  for Point Patterns\nPractical 12 – Spatio-Temporal Models  for Point Patterns\nQ and A and end of day summary\n\n \n \n \n \n \n Friday 26th\n Day 5 – Classes from 14:00 to 21:00 \n\nCase studies\, own data and problem solving.\n\n \n \n \n \n \n \n \n \n Dr Virgillio Gomez Rubio\n \n 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\n\n\nResearchgate\n\nGoogle Scholar\n\nORCID\n\nGitHub URL:https://prstats.org/course/bayesian-modelling-using-r-inla-bmin04/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2025/11/BMIN03.jpg GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260505 DTEND;VALUE=DATE:20260507 DTSTAMP:20260417T101010 CREATED:20260106T161706Z LAST-MODIFIED:20260110T095637Z UID:10000574-1777939200-1778111999@prstats.org SUMMARY:Bayesian Statistical Modelling with Stan and brms (BMSB01) DESCRIPTION:Joint Species Distribution Modelling (JSDM) using HMSC: A Hierarchical Modelling Approach (JSDM01)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, September 22nd\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis 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. \nCourse Program\nTIME 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. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.\n \n \n \n \n \n \n \n \n \n \n Course Details\n 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. \nBy the end of the course participants should be able to: \n\nKnow the different types of spatial and spatio-temporal data available and how to work with them in R.\nKnow the different modelling approaches for spatial and spatio-temporal data.\nKnow how to visualize and produce maps of spatial and spatio-temporal data.\nBe able to fit models with the R-INLA package.\nKnow how to interpret the output from model fitting.\nBe confident with the use of INLA for data analysis.\nUnderstand the different models that can be fit with INLA to spatial and spatio-temporal data.\nKnow how to define the different parts of a model with INLA.\nHave the confidence to use INLA for their own projects.\n\n \n \n \n \n \n Intended Audiences\n \nAcademics 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.\nApplied 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.\nThe 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.\n\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Information\n Time zone – Spain (GMT+2) local time \nAvailability – 20 places \nDuration – 5 days \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n  \nPLEASE 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.\n \n \n \n \n \n Teaching Format\n \n\nThe 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. \n\n\n \n \n \n \n \n Assumed quantitative knowledge\n 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.). \n \n \n \n \n \n \n Assumed computer background\n 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.\n \n \n \n \n \n Equipment and software requirements\n \n\nA 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/. \n\n\nAll 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. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n\n\n  \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n 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. \n\n\n\n \n \n HMSC01 ONLINE\n \n HMSC01 ONLINE\n\n \n \n \n \n £\n 450.00\n \n \n\n \n Unlimited \n \n \n Open the ticket description.\n More \n \n Close the ticket description.\n Less \n \n \n\n \n \n Decrease ticket quantity for HMSC01 ONLINE\n -\n \n \n Quantity \n \n\n \n Increase ticket quantity for HMSC01 ONLINE\n +\n \n \n \n \n\n \n \n Quantity: \n 0\n\n \n \n Total: \n \n \n \n £\n 0.00\n \n \n\n \n Get Tickets\n \n\n \n \n \n\n \n \n\n \n \n\n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations 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.\n \n \n \n \n \n If you are unsure about course suitability\, please get in touch by email to find out more \noliverhooker@prstatistics.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Monday 22nd\n Day 1 – Classes from 14:00 to 21:00 \n\nSession 1 – Intro to INLA\nPractical 1 – Intro to INLA\nSession 2 – Model fitting with INLA\nPractical 2 – Model fitting with INLA\nSession 3 – GLMM’s with INLA\nPractical 3 – GLMM’s with INLA\nQ and A and end of day summary\n\n \n \n \n \n \n Tuesday 23rd\n Day 2 – Classes from 14:00 to 21:00 \n\nSession 4 – Spatial Data\nPractical 4 – Spatial Data\nSession 5 – Spatio-Temporal Data\nPractical 5 – Spatio-Temporal Data\nSession 6 – Advanced Visualisation\nPractical 6 – Advanced Visualisation\nQ and A and end of day summary\n\n \n \n \n \n \n Wednesday 24th\n Day 3 – Classes from 14:00 to 21:00 \n\nSession 7 – Spatial Models for Lattice Data\nPractical 7 – Spatial Models for Lattice Data\nSession 8 – Spatial Models for Continuous Data\nPractical 8 – Spatial Models for Continuous Data\nSession 9 – Spatial Models for Point Patterns\nPractical 9 – Spatial Models for Point Patterns\nQ and A and end of day summary\n\n \n \n \n \n \n Thursday 25th\n Day 4 – Classes from 14:00 to 21:00 \n\nSession 10 – Spatio-Temporal Models for Lattice Data\nPractical 10 – Spatio-Temporal Models for Lattice Data\nSession 11 – Spatio-Temporal Models  for Continuous Data\nPractical 11 – Spatio-Temporal Models  for Continuous Data\nSession 12 – Spatio-Temporal Models  for Point Patterns\nPractical 12 – Spatio-Temporal Models  for Point Patterns\nQ and A and end of day summary\n\n \n \n \n \n \n Friday 26th\n Day 5 – Classes from 14:00 to 21:00 \n\nCase studies\, own data and problem solving.\n\n \n \n \n \n \n \n \n \n Dr Virgillio Gomez Rubio\n \n 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\n\n\nResearchgate\n\nGoogle Scholar\n\nORCID\n\nGitHub URL:https://prstats.org/course/bayesian-statistical-modelling-with-stan-and-brms-bmsb01/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2026/01/BMSB01-1.jpg GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260511 DTEND;VALUE=DATE:20260516 DTSTAMP:20260417T101010 CREATED:20251127T172531Z LAST-MODIFIED:20260408T130749Z UID:10000567-1778457600-1778889599@prstats.org SUMMARY:Movement Ecology (the Analysis of Movement Data) (MOVE09) DESCRIPTION:Joint Species Distribution Modelling (JSDM) using HMSC: A Hierarchical Modelling Approach (JSDM01)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, November 18th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis 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. \nTime Zone\nTIME ZONE – UK (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n About this course\n 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. \nSuch 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. \nAnalysis 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. \nThe 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. \nThe expected outcome of the course is a solid foundation for further professional development via increased confidence in applying these methods for field observations. \nBy the end of the course\, participants should be able to: \n\nUnderstand basic statistical concepts related to detection error\nWork with field collected data and data from automated recording units (ARU)\nKnow packages such as unmarked\, detect\, bSims\nCritically evaluate modelling options and assumptions using simulations\nFit N-mixture\, distance sampling\, and time-removal models to data\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to avian data\nApplied 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\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – UK (GMT) local time \nAvailability – 25 places \nDuration – 3 days\, 4 hours per day \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT \nLanguage – English \n \n \n \n \n \n Teaching Format\n 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. \n \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical\, mathematical and physical concepts. Specifically\, generalised linear regression models\, including mixed models; basic knowledge of calculus. \n \n \n \n \n \n Assumed computer background\n 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. \n \n \n \n \n \n Equipment and software requirements\n \nA 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/. \n\n\nAll 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. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n 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. \n\n\n\n \n \n HMSC01 ONLINE\n \n HMSC01 ONLINE\n\n \n \n \n \n £\n 450.00\n \n \n\n \n Unlimited \n \n \n Open the ticket description.\n More \n \n Close the ticket description.\n Less \n \n \n\n \n \n Decrease ticket quantity for HMSC01 ONLINE\n -\n \n \n Quantity \n \n\n \n Increase ticket quantity for HMSC01 ONLINE\n +\n \n \n \n \n\n \n \n Quantity: \n 0\n\n \n \n Total: \n \n \n \n £\n 0.00\n \n \n\n \n Get Tickets\n \n\n \n \n \n\n \n \n\n \n \n\n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations 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.\n \n \n \n \n \n \n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Tuesday 18th\n Day 1 – Classes from 13:30 – 17:30 \nIntroduction \n\nIntroduction and background\nReview of field sampling techniques\nIntroduction to agent-based simulations\nOverview of regression techniques\nNaïve estimates of occupancy and abundance\nMultiple visits and N-mixture models\n\n \n \n \n \n \n Wednesday 19th\n Day 2 – Classes from 13:30 – 17:30 \nIntroduction to modelling \n\nBird behaviour\nTime-removal models\nObservation process\nDistance sampling\nCombining removal and distance sampling (QPAD)\n\n \n \n \n \n \n Thursday 20th\n Day 3 – Classes from 13:30 – 17:30 \nDifferent approaches \n\nSingle visit-based approaches (N-mixture and SQPAD)\nAnalysing data from recording units\nMulti-species models and using species traits and phylogeny\nDealing with roadside and other biases\nClosing remarks\n\n \n \n \n \n \n \n Course Instructor\n \nDr. Peter Solymos \nPéter 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. \nGoogle Scholar \nWork Homepage \nPersonal Homepage URL:https://prstats.org/course/movement-ecology-the-analysis-of-movement-data-move09/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.org/wp-content/uploads/2025/08/MOVEPR.png GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260511 DTEND;VALUE=DATE:20260516 DTSTAMP:20260417T101010 CREATED:20260127T133239Z LAST-MODIFIED:20260416T144539Z UID:10000584-1778457600-1778889599@prstats.org SUMMARY:Species Distribution Modelling (SDMs) and Ecological Niche Modelling (ENMs) (SDMR07) DESCRIPTION:Joint Species Distribution Modelling (JSDM) using HMSC: A Hierarchical Modelling Approach (JSDM01)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, September 22nd\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis 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. \nTime Zone\nTIME ZONE – Portugal (GMT+1) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).\n \n \n \n \n \n \n \n \n \n \n About This Course\n 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. \nBy the end of the course\, participants should be able to: \n\nCalculate ecological niche models and specie distribution models\nUnderstand their results\, as well as to choose and apply the correct.\nHow to choose the best methodology depending on the aim of their type of study and data.\n\n  \n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to spatial data.\nStudents and researchers working on biogeography\, spatial ecology\, or related disciplines with experience in ecological niche models.\n\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Details\n Time Zone – Portugal (GMT+1) local time \nAvailability – 30 Places \nDuration – 5 days\, 7 hours a day \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English\n \n \n \n \n \n Teaching Format\n 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.\n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical and ecological concepts.\n \n \n \n \n \n Assumed computer background\n 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).\n \n \n \n \n \n Equipment and software requirements\n \nA 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/. \n\n\nAll 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. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n 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. \n\n\n\n \n \n HMSC01 ONLINE\n \n HMSC01 ONLINE\n\n \n \n \n \n £\n 450.00\n \n \n\n \n Unlimited \n \n \n Open the ticket description.\n More \n \n Close the ticket description.\n Less \n \n \n\n \n \n Decrease ticket quantity for HMSC01 ONLINE\n -\n \n \n Quantity \n \n\n \n Increase ticket quantity for HMSC01 ONLINE\n +\n \n \n \n \n\n \n \n Quantity: \n 0\n\n \n \n Total: \n \n \n \n £\n 0.00\n \n \n\n \n Get Tickets\n \n\n \n \n \n\n \n \n\n \n \n\n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations 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.\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Monday 22nd\n Day 1 – Classes from 09:30 to 17:30 \n\nTopic 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.\nTopic 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.\nTopic 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.\n\n \n \n \n \n \n Tuesday 23rd\n Day 2 – Classes from 09:30 to 17:30 \n\nTopic 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.\nTopic 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.\n\n \n \n \n \n \n \n Wednesday 24th\n Day 3 – Classes from 09:30 to 17:30 \n\nTopic 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.\nTopic 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.\n\n \n \n \n \n \n Thursday 25th\n Day 4 – Classes from 09:30 to 17:30 \n\nTopic 8: Modelling with Maxent. Formatting the data\, parameterising Maxent\, calculating the models\, evaluating the models\, projecting the models over time and space.\nTopic 9: Compare statistically two different ecological niche models using the R package ecospat.\n\n \n \n \n \n \n \n Friday 26th\n Day 5 – Classes from 09:30 to 17:30 \n\nTopic 10: Run ecological niche models with your own data.\nTopic 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.\n\n \n \n \n \n \n \n \n \n \n Dr. Neftali Sillero\n \n 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. \nHe 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 \nPersonal website\nWork Webpage\nResearchGate\nGoogleScholar\n \n \n \n \n \n \n \n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM) URL:https://prstats.org/course/species-distribution-modelling-sdms-and-ecological-niche-modelling-enms-sdmr07/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2022/03/SDMR06-1.jpg GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260527 DTEND;VALUE=DATE:20260530 DTSTAMP:20260417T101010 CREATED:20260310T151539Z LAST-MODIFIED:20260310T151601Z UID:10000592-1779840000-1780099199@prstats.org SUMMARY:Interactive Maps with tmap & Shiny (IMTS01) DESCRIPTION:Joint Species Distribution Modelling (JSDM) using HMSC: A Hierarchical Modelling Approach (JSDM01)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, December 1st\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis 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. \nTime Zone\nTIME ZONE – Portugal (GMT+1) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).\n \n \n \n \n \n \n \n \n \n \n About This Course\n 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”. \nEcological 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. \nBy the end of 5-day practical course should be able to: \n\nfilter records and select your variables with variance inflation factor;\ntest the effect of Maxent regularization parameter in models performance;\nvalidate models performance and accuracy;\nperform MESS analysis\, null models\, and mechanistic models\, as well as to build your “virtual species”.\n\nStudents will learn to use functions implemented in the packages “usdm”; “dismo”; “ENMEval”; “SDMvspecies”; “spThin”; and “NicheMapper” among others.\n \n \n \n \n \n Intended Audiences\n 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.\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Details\n Time Zone – Portugal (GMT+!) local time \nAvailability – 24 places \nDuration – 5 days\, 7 hours a day \nContact hours – Approx. 35 hours \nECT’s – Equal to 3ECT’s \nLanguage – English\n \n \n \n \n \n Teaching Format\n 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.\n \n \n \n \n \n Assumed quantitative knowledge\n 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.\n \n \n \n \n \n Assumed computer background\n 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).\n \n \n \n \n \n Equipment and software requirements\n \nA 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/. \n\n\nAll 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. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n 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. \n\n\n\n \n \n HMSC01 ONLINE\n \n HMSC01 ONLINE\n\n \n \n \n \n £\n 450.00\n \n \n\n \n Unlimited \n \n \n Open the ticket description.\n More \n \n Close the ticket description.\n Less \n \n \n\n \n \n Decrease ticket quantity for HMSC01 ONLINE\n -\n \n \n Quantity \n \n\n \n Increase ticket quantity for HMSC01 ONLINE\n +\n \n \n \n \n\n \n \n Quantity: \n 0\n\n \n \n Total: \n \n \n \n £\n 0.00\n \n \n\n \n Get Tickets\n \n\n \n \n \n\n \n \n\n \n \n\n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations 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.\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Monday 1st\n Day 1 – Classes from 09:30 to 17:30 \n\nENM guide: how to model\nENM R packages.\nSources of environmental variables using geodata package.\nGetting species records with geodata package.\n\n \n \n \n \n \n Tuesday 2nd\n Day 2 – Classes from 09:30 to 17:30 \n\nVariable selection with variance inflation factor (VIF) and usdm packages.\nChoosing the correct study area.\nFiltering records using usdm/spThin packages.\nChoosing pseudo-absences with Biomod2 package.\n\n \n \n \n \n \n Wednesday 3rd\n Day 3 – Classes from 09:30 to 17:30 \n\nSplit records in training and test with ENMeval package.\nTest effect of Maxent regularization parameter.<.li>\nComparing correlative models with AIC\, with ENMeval package.\n\n \n \n \n \n \n Thursday 4th\n Day 4 – Classes from 09:30 to 17:30 \n\nMESS practice with Biomod2 package.\nValidate models null models.\nVirtualSpecies virtualspecies packages.\n\n \n \n \n \n \n Friday 5th\n Day 5 – Classes from 09:30 to 17:30 \n\nMechanistic model NicheMapper packages.\n\n \n \n \n \n \n \n \n \n Dr. Neftali Sillero\n \n 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. \nHe 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 \nPersonal website \nWork Webpage \nResearchGate \nGoogleScholar\n \n \n \n \n \n \n \n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n \n \n \n \n \n \n \n \n Dr. Salvador Arenas-Castro\n \n Dr. Salvador Arenas-Castro is a broad-spectrum ecologist with interesting in different\nintegrative perspective of the fundamental ecology\, macroecology and biogeography\nwith their both application and relationship to climate and land management. He is also\nexploring other research sources in agroecology\, forestry\, spatial ecology\, and\necoinformatics\, all addressed by explicitly considering the spatial component of\necological processes\, mainly applying spatially explicit modelling approaches\, GIS and\nremote sensing techniques. Please check his webpage for further information:\nhttps://salvadorarenascastro.wordpress.com \nGoogle Scholar: https://scholar.google.com/citations?user=UAYiB5UAAAAJ&hl=es&oi=ao\nResearchGate: https://www.researchgate.net/profile/Salvador-Arenas-Castro URL:https://prstats.org/course/interactive-maps-with-tmap-shiny-imts01/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.org/wp-content/uploads/2025/07/TMAP02.png GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260601 DTEND;VALUE=DATE:20260604 DTSTAMP:20260417T101010 CREATED:20260130T151942Z LAST-MODIFIED:20260311T161505Z UID:10000587-1780272000-1780531199@prstats.org SUMMARY:Species Distribution Modelling With Bayesian Statistics (SDMB08) DESCRIPTION:Joint Species Distribution Modelling (JSDM) using HMSC: A Hierarchical Modelling Approach (JSDM01)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, September 30th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees\nthrough the accompanying computer practicals via video link\, so a good internet connection is\nessential. \nTime Zone\nTIME ZONE – Portugal (GMT+1) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.\n \n \n \n \n \n \n \n \n \n \n About This Course\n This course focuses on the use of BART (Bayesian Additive Regression Trees) for modelling\nspecies’ 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. \nThe course includes a combination of theoretical lectures and hands-on practicals in R\, as well as\nopen discussions about models and data for SDM applications. The practicals go through a\ncomplete worked example\, from data preparation to model output analysis\, with annotated R\nscripts that can be adapted on-the-spot by participants to work on their own species of interest.\nAlong the course\, the instructor is available for constant feedback and orientation on participants’; outputs and interpretations.\n \n \n \n \n \n Intended Audiences\n The course is aimed at students\, researchers and practitioners with an interest in implementing\nbest practices and state-of-the-art methods for modelling species’ distributions or ecological\nniches\, in an automated and reproducible way.\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Details\n Availability – 18 places \nDuration – 3 days \nContact hours – Approx. 12 hours live\, plus remote assistance via Slack from the first day to the\nweekday after the course. \nECT’s – Equal to 1.5 ECT’s \nLanguage – English\n \n \n \n \n \n Teaching Format\n This course runs along 3 days\, each with a 4-hour live online session. Each session is divided into\n4 parts\, alternating between theoretical lectures and hands-on practicals. Annotated scripts are\nprovided and instructor assistance is available\, both during the live sessions (on Zoom) and\nwhenever possible the rest of the day (on Slack)\, until the weekday after the course.\nLive sessions will be video-recorded\, uploaded to a video hosting website as soon as possible after\neach session\, and remain available for one month after the course.\n \n \n \n \n \n Assumed quantitative knowledge\n Participants should know what species distribution or ecological niche models (SDM / ENM) are\,\nand ideally have some previous experience with the basics. Previous knowledge of Bayesian\nstatistics is not required.\n \n \n \n \n \n Assumed computer background\n Participants should have some previous experience with R\, including package installation and\nbasic data handling\, although commented scripts will be provided for the entire course.\n \n \n \n \n \n Equipment and software requirements\n \nParticipants 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. \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n 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. \n\n\n\n \n \n HMSC01 ONLINE\n \n HMSC01 ONLINE\n\n \n \n \n \n £\n 450.00\n \n \n\n \n Unlimited \n \n \n Open the ticket description.\n More \n \n Close the ticket description.\n Less \n \n \n\n \n \n Decrease ticket quantity for HMSC01 ONLINE\n -\n \n \n Quantity \n \n\n \n Increase ticket quantity for HMSC01 ONLINE\n +\n \n \n \n \n\n \n \n Quantity: \n 0\n\n \n \n Total: \n \n \n \n £\n 0.00\n \n \n\n \n Get Tickets\n \n\n \n \n \n\n \n \n\n \n \n\n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations 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.\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Tuesday 30th\n Day 1 – Classes from 14:00 –  18:00 \n\nModule 1a: Obtain and process data\, including species presences and environmental variables\nPractical\nModule 1b: Determine an adequate spatial resolution and extent for modelling\nPractical\n\n \n \n \n \n \n Wednesday 1st\n Day 2 – Classes from 14:00 –  18:00 \n\nModule 2a: Build a species distribution model with BART and obtain predictions of environmental favorability\, with credibility intervals and associated uncertainty\nPractical\nModule 2b: Evaluate and cross-validate the model\, assessing various aspects of predictive ability\nPractical\n\n \n \n \n \n \n \n Thursday 2nd\n Day 3 – Classes from 14:00 –  18:00 \n\n Module 3a: Quantify variable contributions and try out different methods for selecting relevant variables\nPractical\nModule 3b: Plot and map the species’ partial response to each variable\nPractical\n\n \n \n \n \n \n \n \n \n Dr. Marcia Barbosa\n \n 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. \nResearch Gate \nGoogle Scholar \nORCID \nGitHub \nHomepage URL:https://prstats.org/course/species-distribution-modelling-with-bayesian-statistics-sdmb08/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2024/06/SDMB07-1.jpg GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260601 DTEND;VALUE=DATE:20260605 DTSTAMP:20260417T101010 CREATED:20250714T093009Z LAST-MODIFIED:20260313T125928Z UID:10000483-1780272000-1780617599@prstats.org SUMMARY:Python for Biological Data Exploration and Visualization (PYBD01) DESCRIPTION:Joint Species Distribution Modelling (JSDM) using HMSC: A Hierarchical Modelling Approach (JSDM01)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, December 1st\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis 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. \nTime Zone\nTIME ZONE – Portugal (GMT+1) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).\n \n \n \n \n \n \n \n \n \n \n About This Course\n 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”. \nEcological 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. \nBy the end of 5-day practical course should be able to: \n\nfilter records and select your variables with variance inflation factor;\ntest the effect of Maxent regularization parameter in models performance;\nvalidate models performance and accuracy;\nperform MESS analysis\, null models\, and mechanistic models\, as well as to build your “virtual species”.\n\nStudents will learn to use functions implemented in the packages “usdm”; “dismo”; “ENMEval”; “SDMvspecies”; “spThin”; and “NicheMapper” among others.\n \n \n \n \n \n Intended Audiences\n 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.\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Details\n Time Zone – Portugal (GMT+!) local time \nAvailability – 24 places \nDuration – 5 days\, 7 hours a day \nContact hours – Approx. 35 hours \nECT’s – Equal to 3ECT’s \nLanguage – English\n \n \n \n \n \n Teaching Format\n 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.\n \n \n \n \n \n Assumed quantitative knowledge\n 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.\n \n \n \n \n \n Assumed computer background\n 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).\n \n \n \n \n \n Equipment and software requirements\n \nA 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/. \n\n\nAll 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. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n 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. \n\n\n\n \n \n HMSC01 ONLINE\n \n HMSC01 ONLINE\n\n \n \n \n \n £\n 450.00\n \n \n\n \n Unlimited \n \n \n Open the ticket description.\n More \n \n Close the ticket description.\n Less \n \n \n\n \n \n Decrease ticket quantity for HMSC01 ONLINE\n -\n \n \n Quantity \n \n\n \n Increase ticket quantity for HMSC01 ONLINE\n +\n \n \n \n \n\n \n \n Quantity: \n 0\n\n \n \n Total: \n \n \n \n £\n 0.00\n \n \n\n \n Get Tickets\n \n\n \n \n \n\n \n \n\n \n \n\n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations 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.\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n \n Monday 1st\n Day 1 – Classes from 09:30 to 17:30 \n\nENM guide: how to model\nENM R packages.\nSources of environmental variables using geodata package.\nGetting species records with geodata package.\n\n \n \n \n \n \n Tuesday 2nd\n Day 2 – Classes from 09:30 to 17:30 \n\nVariable selection with variance inflation factor (VIF) and usdm packages.\nChoosing the correct study area.\nFiltering records using usdm/spThin packages.\nChoosing pseudo-absences with Biomod2 package.\n\n \n \n \n \n \n Wednesday 3rd\n Day 3 – Classes from 09:30 to 17:30 \n\nSplit records in training and test with ENMeval package.\nTest effect of Maxent regularization parameter.<.li>\nComparing correlative models with AIC\, with ENMeval package.\n\n \n \n \n \n \n Thursday 4th\n Day 4 – Classes from 09:30 to 17:30 \n\nMESS practice with Biomod2 package.\nValidate models null models.\nVirtualSpecies virtualspecies packages.\n\n \n \n \n \n \n Friday 5th\n Day 5 – Classes from 09:30 to 17:30 \n\nMechanistic model NicheMapper packages.\n\n \n \n \n \n \n \n \n \n Dr. Neftali Sillero\n \n 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. \nHe 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 \nPersonal website \nWork Webpage \nResearchGate \nGoogleScholar\n \n \n \n \n \n \n \n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n \n \n \n \n \n \n \n \n Dr. Salvador Arenas-Castro\n \n Dr. Salvador Arenas-Castro is a broad-spectrum ecologist with interesting in different\nintegrative perspective of the fundamental ecology\, macroecology and biogeography\nwith their both application and relationship to climate and land management. He is also\nexploring other research sources in agroecology\, forestry\, spatial ecology\, and\necoinformatics\, all addressed by explicitly considering the spatial component of\necological processes\, mainly applying spatially explicit modelling approaches\, GIS and\nremote sensing techniques. Please check his webpage for further information:\nhttps://salvadorarenascastro.wordpress.com \nGoogle Scholar: https://scholar.google.com/citations?user=UAYiB5UAAAAJ&hl=es&oi=ao\nResearchGate: https://www.researchgate.net/profile/Salvador-Arenas-Castro URL:https://prstats.org/course/python-for-biological-data-exploration-and-visualization-pybd01/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:Home Courses,Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2025/07/PYBD01.jpg GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260601 DTEND;VALUE=DATE:20260606 DTSTAMP:20260417T101010 CREATED:20260114T153444Z LAST-MODIFIED:20260114T153454Z UID:10000581-1780272000-1780703999@prstats.org SUMMARY:Introduction to Processing and Analysis of Spatial Multiplexed Proteomics Data (SPMP02) DESCRIPTION:Joint Species Distribution Modelling (JSDM) using HMSC: A Hierarchical Modelling Approach (JSDM01)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, October 6th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis 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. \nTime Zone\nTIME ZONE – UK (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.\n \n \n \n \n \n \n \n \n \n \n About this course\n 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. \nBy the end of the course\, participants should be able to: \n\nUnderstand the basic concepts of bioacoustics and how animal acoustic signals are analyzed\nGain proficiency in handling and manipulating acoustic data in R\, including working with ‘wave’ objects and other audio formats\nDevelop skills in building and interpreting spectrograms using Fourier transform techniques and the seewave package in R\nImport Raven Pro annotations into R and refine these annotations with warbleR functions\nUnderstand how to quantify the structure of acoustic signals through various approaches\nGain experience in quality control of recordings and annotations\, ensuring data integrity and accuracy\nCompare different methods for quantifying acoustic signal structure and understand the implications of each approach\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students conducting research in bioacoustics\, animal behavior\, ecology\, or related fields\nApplied researchers and analysts in public\, private\, or non-profit organizations who require robust\, reproducible\, and flexible tools for analyzing acoustic data\nCurrent R users seeking to expand their knowledge into the field of bioacoustics and learn how to utilize specialized packages for acoustic analysis\nWildlife biologists\, and conservationists interested in leveraging bioacoustic methods for species monitoring and behavioral studies\nData scientists and programmers interested in applying their coding skills to the analysis of animal acoustic signals\n\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Details\n Time Zone – UK (GMT) local time \nAvailability – 20 places \nDuration – 5 days\, 4 hours per day \nContact hours – Approx. 20 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English\n \n \n \n \n \n Teaching Format\n 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. \n\n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical concepts. Specifically\, generalised linear regression models\, statistical significance\, hypothesis testing.\n \n \n \n \n \n Assumed computer background\n Familiarity with R. Ability to import/export data\, manipulate data frames\, fit basic statistical models & generate simple exploratory and diagnostic plots.\n \n \n \n \n \n Equipment and software requirements\n \nA 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/. \n\n\nAll 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. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n 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. \n\n\n\n \n \n HMSC01 ONLINE\n \n HMSC01 ONLINE\n\n \n \n \n \n £\n 450.00\n \n \n\n \n Unlimited \n \n \n Open the ticket description.\n More \n \n Close the ticket description.\n Less \n \n \n\n \n \n Decrease ticket quantity for HMSC01 ONLINE\n -\n \n \n Quantity \n \n\n \n Increase ticket quantity for HMSC01 ONLINE\n +\n \n \n \n \n\n \n \n Quantity: \n 0\n\n \n \n Total: \n \n \n \n £\n 0.00\n \n \n\n \n Get Tickets\n \n\n \n \n \n\n \n \n\n \n \n\n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations 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.\n \n \n \n \n \n \n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Monday 6th\n Day 1 – Classes from 13:30 – 17:30 \nIntroduction \n\nHow animal acoustic signals look like?\nAn 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.\nAnalytical workflow in bioacoustics research\nIntroduction to the step-by-step process involved in bioacoustic research\, from recording and\ndata collection to analysis and interpretation. This session will outline the typical workflow\,\nemphasizing the importance of each step.\nAdvantages of programming\nDiscussion on the benefits of using programming languages like R for bioacoustic analysis\,\nincluding reproducibility\, efficiency\, and the ability to handle large datasets. This will highlight\nhow programming can enhance research capabilities.\n\n  \nWhat is sound? \n\nSound as a time series\nExplanation of how sound can be represented as a time series\, with each point in the series\nrepresenting the sound pressure level at a given moment in time. This forms the basis for further analysis and manipulation.\nSound as a digital object\nDiscussion 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.\nAcoustic data in R\nIntroduction to handling and analyzing acoustic data in R. This includes importing sound files\, basic data exploration\, and visualization techniques.\n‘wave’ object structure\nExplanation of the ‘wave’ object in R\, its structure\, and the information it contains. This is\nessential for understanding how to manipulate and analyze sound data in R.\n‘wave’ object manipulations\nTechniques for manipulating ‘wave’ objects\, including trimming\, concatenating\, and modifying sound files. Practical exercises will be provided to reinforce these concepts.\nAdditional formats\nOverview of other audio file formats (e.g.\, MP3\, FLAC) and how they can be converted and used in R for bioacoustic analysis.\n\n \n \n \n \n \n Tuesday 7th\n Day 2 – Classes from 13:30 – 17:30 \nBuilding spectrograms \n\nFourier transform\nExplanation of the Fourier transform and its application in converting time-domain signals into\nfrequency-domain representations. This is the foundation for creating spectrograms.\nBuilding a spectrogram\nStep-by-step guide on how to construct spectrograms\, including parameter selection (e.g.\,\nwindow size\, overlap) and interpretation of the resulting visual representations.\nCharacteristics and limitations\nDiscussion on the strengths and limitations of spectrograms\, including resolution trade-offs and potential artifacts. Participants will learn to critically evaluate spectrograms.\nSpectrograms in R\nPractical session on generating and customizing spectrograms in R using the seewave package.\nParticipants will create spectrograms from their own data.\nPackage seewave\nExplore\, modify and measure ‘wave’ objects\nHands-on exploration of the seewave package\, focusing on functions for modifying and\nmeasuring 'wave' objects. This includes exercises on filtering\, re-sampling\, and extracting acoustic features.\nSpectrograms and oscillograms\nCreating and interpreting both spectrograms and oscillograms in R. Participants will learn to\nvisualize sound data in different ways to highlight various aspects of the signal.\nFiltering and re-sampling\nTechniques for filtering (e.g.\, band-pass\, high-pass) and re-sampling sound files to focus on\nspecific frequency ranges or standardize sampling rates.\nAcoustic measurements\nUsing the seewave package to perform detailed acoustic measurements\, such as peak frequency\, dominant frequency\, and frequency range. Practical examples will be provided.\n\n \n \n \n \n \n Wednesday 8th\n Day 3 – Classes from 13:30 – 17:30 \nAnnotations \n\nIntroduction to the Raven Pro Interface\nA guided tour of the Raven Pro software\, its main features\, and interface elements. Participants will learn how to navigate the software efficiently.\nIntroduction to selections and measurements\nInstruction on how to make selections within sound files and take basic measurements such as duration and frequency using Raven Pro.\nSaving\, retrieving\, and exporting selection tables\nHow to save\, retrieve\, and export selection tables in Raven Pro for further analysis. This session will cover best practices for data management and organization.\nUsing annotations\nTechniques for annotating sound files in Raven Pro\, including the use of labels and notes to mark significant events or features within the recordings.\n\n  \nQuantifying acoustic signal structure \n\nSpectro-temporal measurements (spectro_analysis())\nIntroduction to the spectro_analysis() function in R for extracting spectro-temporal\nmeasurements from audio recordings. Participants will learn to describe acoustic signals in terms of their temporal and spectral characteristics.\nParameter description\nDetailed explanation of key acoustic parameters\, such as duration\, frequency range\, and\namplitude\, and how they are used to describe sound signals.\nHarmonic content\nTechniques for analyzing the harmonic content of signals\, including identifying harmonic series and measuring harmonic-to-noise ratios.\nCepstral coefficients (mfcc_stats())\nIntroduction 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.\nCross-correlation (cross_correlation())\nExplanation of cross-correlation techniques for comparing sound signals. Participants will use cross_correlation() to measure the similarity between different recordings.\nDynamic time warping (freq_DTW())\nIntroduction 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.\nSignal-to-noise ratio (sig2noise())\nTechniques for calculating the signal-to-noise ratio (SNR) of recordings\, which is crucial for\nassessing the quality of sound data.\nInflections (inflections())\nIdentifying and measuring inflections in sound signals\, which can indicate changes in pitch or other dynamic features.\nParameters at other levels (song_analysis())\nExploring acoustic parameters at higher hierarchical levels\, such as entire songs or sequences of vocalizations\, using the song_analysis() function.\n\n \n \n \n \n \n Thursday 9th\n Day 4 – Classes from 13:30 – 17:30 \nQuality control in recordings and annotations \n\nCreate catalogs\nCompiling catalogs of annotated sound files\, which can be used for further analysis or as\nreference materials.\nCheck and modify sound file format (check_wavs()\, info_wavs()\, duration_wavs()\,\nmp32wav() y fix_wavs())\nTechniques for checking and modifying sound file formats using various functions in R. This\nincludes converting files\, checking file integrity\, and fixing common issues.\nTuning spectrogram parameters (tweak_spectro())\nAdjusting spectrogram parameters to optimize the visualization and analysis of sound signals.\nParticipants will use tweak_spectro() to fine-tune their spectrograms.\nDouble-checking selection tables (check_sels()\, spectrograms()\, full_spectrograms() &\ncatalog())\nMethods for verifying and refining selection tables\, ensuring that all annotations are accurate and comprehensive.\nRe-adjusting selections (tailor_sels())\nTechniques for re-adjusting selections in response to quality control checks\, ensuring that all\nannotations are precise and correctly positioned.\nCharacterizing hierarchical levels in acoustic signals\nCreating ‘song’ spectrograms (full_spectrograms()\, spectrograms())\nBuilding spectrograms that represent entire songs or sequences of vocalizations\, providing a\nhigher-level view of acoustic patterns.\n‘Song’ parameters (song_analysis())\nMeasuring and analyzing parameters at the song level\, such as song duration\, number of\nelements and element rate\, using the song_analysis() function.\n\n \n \n \n \n \n Friday 10th\n Day 5 – Classes from 13:30 – 17:30 \nChoosing the right method for quantifying structure \n\nCompare different methods for quantifying structure (compare_methods())\nComparing various methods for quantifying acoustic signal structure. Participants will use\ncompare_methods() to evaluate different approaches.\nQuantifying acoustic spaces\nIntro to PhenotypeSpace\nIntroduction to the concept of acoustic spaces and the PhenotypeSpace framework\, which allows for the visualization and comparison of acoustic diversity.\nQuantifying space size\nTechniques for measuring the size of acoustic spaces\, which can provide insights into the\nvariability and complexity of vocalizations.\nComparing sub-spaces\nMethods for comparing different sub-spaces within the overall acoustic space\, allowing for the analysis of variations between species\, populations\, or other groups.\nEach 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.\n\n \n \n \n \n \n \n Course Instructor\n \n*\nDr. Marcelo Araya Salas\nWorks at – Neuroscience Research Center\, Universidad de Costa Rica \nMarcelo 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. \nResearchGate \nGoogle Scholar \nWork Homepage \nPersonal Homepage URL:https://prstats.org/course/introduction-to-processing-and-analysis-of-spatial-multiplexed-proteomics-data-spmp02/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.org/wp-content/uploads/2025/10/SPMP01.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260713 DTEND;VALUE=DATE:20260717 DTSTAMP:20260417T101010 CREATED:20250716T193749Z LAST-MODIFIED:20260220T184729Z UID:10000489-1783900800-1784246399@prstats.org SUMMARY:Reproducible Spatial Ecology: Visualization\, Reporting\, and AI-Assisted Workflows with R (RSPE01) DESCRIPTION:Joint Species Distribution Modelling (JSDM) using HMSC: A Hierarchical Modelling Approach (JSDM01)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, December 1st\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis 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. \nTime Zone\nTIME ZONE – Portugal (GMT+1) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n \n \n \n \n \n \n \n \n \n \n About This Course\n 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”. \nEcological 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. \nBy the end of 5-day practical course should be able to: \n\nfilter records and select your variables with variance inflation factor;\ntest the effect of Maxent regularization parameter in models performance;\nvalidate models performance and accuracy;\nperform MESS analysis\, null models\, and mechanistic models\, as well as to build your “virtual species”.\n\nStudents will learn to use functions implemented in the packages “usdm”; “dismo”; “ENMEval”; “SDMvspecies”; “spThin”; and “NicheMapper” among others. \n \n \n \n \n \n Intended Audiences\n 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. \n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Details\n Time Zone – Portugal (GMT+!) local time \nAvailability – 24 places \nDuration – 5 days\, 7 hours a day \nContact hours – Approx. 35 hours \nECT’s – Equal to 3ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n 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. \n \n \n \n \n \n Assumed quantitative knowledge\n 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. \n \n \n \n \n \n Assumed computer background\n 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). \n \n \n \n \n \n Equipment and software requirements\n \nA 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/. \n\n\nAll 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. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n 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. \n\n\n\n \n \n HMSC01 ONLINE\n \n HMSC01 ONLINE\n\n \n \n \n \n £\n 450.00\n \n \n\n \n Unlimited \n \n \n Open the ticket description.\n More \n \n Close the ticket description.\n Less \n \n \n\n \n \n Decrease ticket quantity for HMSC01 ONLINE\n -\n \n \n Quantity \n \n\n \n Increase ticket quantity for HMSC01 ONLINE\n +\n \n \n \n \n\n \n \n Quantity: \n 0\n\n \n \n Total: \n \n \n \n £\n 0.00\n \n \n\n \n Get Tickets\n \n\n \n \n \n\n \n \n\n \n \n\n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations 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.\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n  \n \n \n \n \n \n \n \n \n \n \n Monday 1st\n Day 1 – Classes from 09:30 to 17:30 \n\nENM guide: how to model\nENM R packages.\nSources of environmental variables using geodata package.\nGetting species records with geodata package.\n\n \n \n \n \n \n Tuesday 2nd\n Day 2 – Classes from 09:30 to 17:30 \n\nVariable selection with variance inflation factor (VIF) and usdm packages.\nChoosing the correct study area.\nFiltering records using usdm/spThin packages.\nChoosing pseudo-absences with Biomod2 package.\n\n \n \n \n \n \n Wednesday 3rd\n Day 3 – Classes from 09:30 to 17:30 \n\nSplit records in training and test with ENMeval package.\nTest effect of Maxent regularization parameter.<.li>\nComparing correlative models with AIC\, with ENMeval package.\n\n \n \n \n \n \n Thursday 4th\n Day 4 – Classes from 09:30 to 17:30 \n\nMESS practice with Biomod2 package.\nValidate models null models.\nVirtualSpecies virtualspecies packages.\n\n \n \n \n \n \n Friday 5th\n Day 5 – Classes from 09:30 to 17:30 \n\nMechanistic model NicheMapper packages.\n\n \n \n \n \n \n \n \n \n Dr. Neftali Sillero\n \n 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. \nHe 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 \nPersonal website \nWork Webpage \nResearchGate \nGoogleScholar \n \n \n \n \n \n \n \n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n \n \n \n \n \n \n \n \n Dr. Salvador Arenas-Castro\n \n 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 \nGoogle Scholar: https://scholar.google.com/citations?user=UAYiB5UAAAAJ&hl=es&oi=aoResearchGate: https://www.researchgate.net/profile/Salvador-Arenas-Castro URL:https://prstats.org/course/reproducible-spatial-ecology-rspe01/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2025/07/VSED01-copy.jpg GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260930 DTEND;VALUE=DATE:20261003 DTSTAMP:20260417T101010 CREATED:20260310T153043Z LAST-MODIFIED:20260416T142952Z UID:10000593-1790726400-1790985599@prstats.org SUMMARY:Advanced Static Cartography in R (ASCR01) DESCRIPTION:Joint Species Distribution Modelling (JSDM) using HMSC: A Hierarchical Modelling Approach (JSDM01)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, December 1st\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis 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. \nTime Zone\nTIME ZONE – Portugal (GMT+1) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).\n \n \n \n \n \n \n \n \n \n \n About This Course\n 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”. \nEcological 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. \nBy the end of 5-day practical course should be able to: \n\nfilter records and select your variables with variance inflation factor;\ntest the effect of Maxent regularization parameter in models performance;\nvalidate models performance and accuracy;\nperform MESS analysis\, null models\, and mechanistic models\, as well as to build your “virtual species”.\n\nStudents will learn to use functions implemented in the packages “usdm”; “dismo”; “ENMEval”; “SDMvspecies”; “spThin”; and “NicheMapper” among others.\n \n \n \n \n \n Intended Audiences\n 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.\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Details\n Time Zone – Portugal (GMT+!) local time \nAvailability – 24 places \nDuration – 5 days\, 7 hours a day \nContact hours – Approx. 35 hours \nECT’s – Equal to 3ECT’s \nLanguage – English\n \n \n \n \n \n Teaching Format\n 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.\n \n \n \n \n \n Assumed quantitative knowledge\n 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.\n \n \n \n \n \n Assumed computer background\n 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).\n \n \n \n \n \n Equipment and software requirements\n \nA 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/. \n\n\nAll 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. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n 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. \n\n\n\n \n \n HMSC01 ONLINE\n \n HMSC01 ONLINE\n\n \n \n \n \n £\n 450.00\n \n \n\n \n Unlimited \n \n \n Open the ticket description.\n More \n \n Close the ticket description.\n Less \n \n \n\n \n \n Decrease ticket quantity for HMSC01 ONLINE\n -\n \n \n Quantity \n \n\n \n Increase ticket quantity for HMSC01 ONLINE\n +\n \n \n \n \n\n \n \n Quantity: \n 0\n\n \n \n Total: \n \n \n \n £\n 0.00\n \n \n\n \n Get Tickets\n \n\n \n \n \n\n \n \n\n \n \n\n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations 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.\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Monday 1st\n Day 1 – Classes from 09:30 to 17:30 \n\nENM guide: how to model\nENM R packages.\nSources of environmental variables using geodata package.\nGetting species records with geodata package.\n\n \n \n \n \n \n Tuesday 2nd\n Day 2 – Classes from 09:30 to 17:30 \n\nVariable selection with variance inflation factor (VIF) and usdm packages.\nChoosing the correct study area.\nFiltering records using usdm/spThin packages.\nChoosing pseudo-absences with Biomod2 package.\n\n \n \n \n \n \n Wednesday 3rd\n Day 3 – Classes from 09:30 to 17:30 \n\nSplit records in training and test with ENMeval package.\nTest effect of Maxent regularization parameter.<.li>\nComparing correlative models with AIC\, with ENMeval package.\n\n \n \n \n \n \n Thursday 4th\n Day 4 – Classes from 09:30 to 17:30 \n\nMESS practice with Biomod2 package.\nValidate models null models.\nVirtualSpecies virtualspecies packages.\n\n \n \n \n \n \n Friday 5th\n Day 5 – Classes from 09:30 to 17:30 \n\nMechanistic model NicheMapper packages.\n\n \n \n \n \n \n \n \n \n Dr. Neftali Sillero\n \n 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. \nHe 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 \nPersonal website \nWork Webpage \nResearchGate \nGoogleScholar\n \n \n \n \n \n \n \n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n \n \n \n \n \n \n \n \n Dr. Salvador Arenas-Castro\n \n Dr. Salvador Arenas-Castro is a broad-spectrum ecologist with interesting in different\nintegrative perspective of the fundamental ecology\, macroecology and biogeography\nwith their both application and relationship to climate and land management. He is also\nexploring other research sources in agroecology\, forestry\, spatial ecology\, and\necoinformatics\, all addressed by explicitly considering the spatial component of\necological processes\, mainly applying spatially explicit modelling approaches\, GIS and\nremote sensing techniques. Please check his webpage for further information:\nhttps://salvadorarenascastro.wordpress.com \nGoogle Scholar: https://scholar.google.com/citations?user=UAYiB5UAAAAJ&hl=es&oi=ao\nResearchGate: https://www.researchgate.net/profile/Salvador-Arenas-Castro URL:https://prstats.org/course/advanced-static-cartography-in-r-ascr01/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.org/wp-content/uploads/2025/10/GLMG01.png GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20261026 DTEND;VALUE=DATE:20261030 DTSTAMP:20260417T101011 CREATED:20260409T115422Z LAST-MODIFIED:20260410T110245Z UID:10000600-1792972800-1793318399@prstats.org SUMMARY:Joint Species Distribution Modelling (JSDM) using HMSC: A Hierarchical Modelling Approach (JSDM01) DESCRIPTION: URL:https://prstats.org/course/joint-species-distribution-modelling-jsdm01/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2025/08/HMMMPR.jpg GEO:53.1423672;-7.6920536 END:VEVENT END:VCALENDAR