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BEGIN:VEVENT
DTSTART;VALUE=DATE:20260407
DTEND;VALUE=DATE:20360409
DTSTAMP:20260420T022800
CREATED:20260413T113721Z
LAST-MODIFIED:20260413T120624Z
UID:10000610-1775520000-2091311999@prstats.org
SUMMARY:Python for Data Science and Statistical Computing (PYDSPR)
DESCRIPTION:
URL:https://prstats.org/course/python-for-data-science-and-statistical-computing-pydspr/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom
CATEGORIES:General Recorded Courses,Previously Recorded Courses
ATTACH;FMTTYPE=image/png:https://prstats.org/wp-content/uploads/2025/11/PYDS01.png
GEO:53.1423672;-7.6920536
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260409
DTEND;VALUE=DATE:20360410
DTSTAMP:20260420T022800
CREATED:20260409T161126Z
LAST-MODIFIED:20260410T080622Z
UID:10000606-1775692800-2091398399@prstats.org
SUMMARY:Mechanistic Species Distribution Modelling / Ecological Niche Modelling with NicheMapR (MSDMPR)
DESCRIPTION:
URL:https://prstats.org/course/mechanistic-species-distribution-modelling-ecological-niche-modelling-with-nichemapr-msdmpr/
CATEGORIES:Previously Recorded Courses,Spatial Ecology
ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2026/01/SDMS01-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260410
DTEND;VALUE=DATE:20360411
DTSTAMP:20260420T022800
CREATED:20260410T075640Z
LAST-MODIFIED:20260414T115745Z
UID:10000608-1775779200-2091484799@prstats.org
SUMMARY:Model Validation for Species Distribution and Ecological Niche Modelling (MVSDPR)
DESCRIPTION:
URL:https://prstats.org/course/model-validation-for-species-distribution-and-ecological-niche-modelling-mvsdpr/
CATEGORIES:Previously Recorded Courses,Spatial Ecology
ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2026/01/SDMS01-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260410
DTEND;VALUE=DATE:20360411
DTSTAMP:20260420T022800
CREATED:20260410T101455Z
LAST-MODIFIED:20260410T101646Z
UID:10000609-1775779200-2091484799@prstats.org
SUMMARY:Advanced Python for Ecologists and Evolutionary Biologists (APYBPR)
DESCRIPTION:
URL:https://prstats.org/course/advanced-python-for-ecologists-and-evolutionary-biologists-apybpr/
CATEGORIES:Molecular Ecology,Previously Recorded Courses
ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2025/04/AYPB01.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260420
DTEND;VALUE=DATE:20260424
DTSTAMP:20260420T022800
CREATED:20251127T171752Z
LAST-MODIFIED:20260408T130542Z
UID:10000566-1776643200-1776988799@prstats.org
SUMMARY:Analysing Ecological Data with Detection Error (AEDD01)
DESCRIPTION:Python for Data Science and Statistical Computing (PYDSPR)\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		PYDSPR RECORDED\n	\n	PYDSPR RECORDED\n\n	\n		\n		\n				\n					£\n					300.00\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 PYDSPR RECORDED\n	-\n		\n	\n		Quantity	\n	\n\n		\n	Increase ticket quantity for PYDSPR RECORDED\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:20260420
DTEND;VALUE=DATE:20260425
DTSTAMP:20260420T022800
CREATED:20260109T132757Z
LAST-MODIFIED:20260112T112628Z
UID:10000577-1776643200-1777075199@prstats.org
SUMMARY:Multivariate Analysis of Ecological Communities Using VEGAN (VGNR09)
DESCRIPTION:Python for Data Science and Statistical Computing (PYDSPR)\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nMonday\, September 15\, 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 – Reunion (GMT+4) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow.\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				About This Course\n				This 5-day course covers R concepts\, methods\, and tools that can be used to analyse community ecology data using (but not limited to) the R package VEGAN. The course will review data processing techniques relevant to multivariate data sets. We will cover diversity indices\, distance measures and distance-based multivariate methods\, clustering\, classification and ordination techniques using the R package VEGAN. We will use real-world empirical data sets to motivate analyses\, such as describing patterns along gradients of environ-mental or anthropogenic disturbances\, quantifying the effects of continuous and discrete predictors. We will emphasise visualisation and reproducible workflows as well as good programming practices. The modules will consist of introductory lectures\, guided computer coding\, and participant exercises. The course is intended for intermediate users of R who are interested in community ecology\, particularly in the areas of terrestrial and wetland ecology\, microbial ecology\, and natural resource management. You are strongly encouraged to use your own data sets (they should be clean and already structured\, see the document: “recommendation if you participate with your data”. \nWe will cover the following:\n\n\nFundamentals of community ecology\,\nDiversity indices\,\nMethods to transform data and calculate distance measures\,\nClassifications (i.e.\, clustering methods) organise the data into synthetic groups and present them in a tree (dendrogram).\nOrdinations (i.e.\, unconstrained methods) reveal the multivariate dimension in only a few dimensions (axes).\nCanonical ordinations (i.e.\, constrained methods) test hypotheses related to multivariate patterns.\n\n\n\nIn addition the course provides lectures and practices on how to create reproducible workflows and use good programming practices in R.\n\nTopics covered during the course include: terrestrial and wetland ecology\, microbial ecology\, and natural resource management\, evolution\, palaeoecology.\n\n\n\nDuring the workshops you will follow guided computer coding exercises using either your own data or real empirical datasets to motivate analyses. Exercises include describing patterns along gradients of environmental or anthropogenic disturbance\, quantifying the effects of continuous and discrete predictors.\n\nYou are strongly encouraged to use your own datasets (they should be clean and already structured\, please contact use if you plan to do this\, we will help you to prepare the data). You will benefit from full support in applying multivariate methods to your dataset (defining of the research question\, transforming your data\, selecting the most appropriate method\, carrying out the analysis and interpreting the results).\n\n\n\n			\n				\n				\n				\n				\n				Intended Audiences\n				Any researchers (PhD and MSc students\, post-docs\, primary investigators) and environmental professionals who are interested in implementing best practices and state-of-the-art methods for modelling species’ distributions or ecological niches\, with applications to biogeography\, spatial ecology\, biodiversity conservation and related disciplines.\n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely\n			\n				\n				\n				\n				\n				Course Details\n				Time Zone – Reunion Island (GMT+4) local time \nAvailability – 20 places \nDuration – 5 days\, 8 hours a day \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English (with the option to discuss individually in French)\n			\n				\n				\n				\n				\n				Teaching Format\n				The course will be divided into theoretical lectures to introduce and explain key concepts and theories\, and practices with workshop sessions on R. We will cover roughly 2 modules per day\, each module consists of ~1h30/2h lecture + coding\, break\, ~1h30/2h exercises + summary/discussion. \nThe schedule can be slightly modified according to the interest of the participants and to accommodate different timezones.\n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				We will assume that you are familiar with basic statistical concepts\, linear models\, and statistical tests (the equivalent of an undergraduate introductory statistics course will be sufficient to follow the course).\n			\n				\n				\n				\n				\n				Assumed computer background\n				To take full advantage of this course\, minimal prior experience with R is required. Participants should be familiar with basic R syntax and commands\, know how to write code in the RStudio console and script editor\, load data from files (txt\, xls\, csv).\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		PYDSPR RECORDED\n	\n	PYDSPR RECORDED\n\n	\n		\n		\n				\n					£\n					300.00\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 PYDSPR RECORDED\n	-\n		\n	\n		Quantity	\n	\n\n		\n	Increase ticket quantity for PYDSPR RECORDED\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				\n				\n				\n				\n				\n				Monday 15th\n				Day 1 – Classes from 08:00 – 13:00 and 14:00 – 16:00 \n• Module 1: Introduction to community data analysis\, basics of programming in R\n• Module 2: Diversity analysis\, species-abundance distributions \n			\n				\n				\n				\n				\n				Tuesday 16th\n				Day 2 – Classes from 08:00 – 13:00 and 14:00 – 16:00 \n• Module 3: Distance and transformation measures\n• Module 4: Clustering and classification analysis \n			\n				\n				\n				\n				\n				Wednesday 17th\n				Day 3 – Classes from 08:00 – 13:00 and 14:00 – 16:00 \n• Module 5: Unconstrained ordinations: Principal Component Analysis\n• Module 6: Other unconstrained ordinations \n			\n				\n				\n				\n				\n				Thursday 18th\n				Day 4 – Classes from 08:00 – 13:00 and 14:00 – 16:00 \n• Module 7: Constrained ordinations: RDA and other canonical analysis\n• Module 8: Statistical tests for multivariate data and variation partitioning \n			\n				\n				\n				\n				\n				Friday 19th\n				Day 5 – Classes from 08:00 – 13:00 and 14:00 – 16:00 \n• Module 9: Overview of Spatial analysis\, and recent Hierarchical Modeling of Species Communities (HMSC) methods\n• Modules 10: Special topics and discussion\, analyzing participants’ data. \n			\n			\n				\n				\n				\n				\n				\n				\n					Antoine Becker-Scarpitta\n					\n					Antoine is a community ecologist and forest ecologist working as a researcher at The French agricultural research and international cooperation organization\, working for the sustainable development of tropical and Mediterranean regions. Antoine was a postdoctoral researcher at the University of Helsinki and the Institute of Botany of the Academy of the Czech Republic. He holds a degree in Conservation Biology from the University of Paris-Sud-Orsay\, and he obtained his PhD in Biology/Ecology from the University of Sherbrooke (Canada). Antoine’s research focuses on the temporal dynamics of biodiversity\, particularly on the forest and Arctic vegetation. Antoine has taught community ecology\, plant ecology and evolution\, linear and multivariate statistics assisted on R. \nResearchGate \nGoogle Scholar \nORCID \nGitHub
URL:https://prstats.org/course/multivariate-analysis-of-ecological-communities-using-vegan-vgnr09/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom
CATEGORIES:All Live Courses,Live Online Courses
ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2021/12/VGNR08-1.jpg
GEO:53.1423672;-7.6920536
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