BEGIN:VCALENDAR VERSION:2.0 PRODID:-//PR Stats - ECPv6.15.20//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH 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:20260316 DTEND;VALUE=DATE:20260320 DTSTAMP:20260417T030309 CREATED:20251119T164140Z LAST-MODIFIED:20260310T201235Z UID:10000560-1773619200-1773964799@prstats.org SUMMARY:Single cell RNA-Seq analysis (SCRN02) 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/single-cell-rna-seq-analysis-scrn02/ 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/2025/09/SCRN01.jpg GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260323 DTEND;VALUE=DATE:20260327 DTSTAMP:20260417T030309 CREATED:20250430T211430Z LAST-MODIFIED:20260410T101613Z UID:10000479-1774224000-1774569599@prstats.org SUMMARY:Advanced Python for Ecologists and Evolutionary Biologists (APYB01) 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\, 28th October\, 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 instructors will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCOURSE PROGRAM\n\nTIME ZONE – UK (GMT+1) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \n\n\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 \n About This Course\n Python is a dynamic\, readable language that is a popular platform for all types of  bioinformatics work\, from simple one-off scripts to large\, complex  software projects. This workshop aims to give novice programmers an introduction to using Python for research in evolutionary biology and genomics by using biological examples throughout. We will use example datasets and problems themed around sequence analysis\, taxonomy and ecology\, with plenty of time for participants to work on their own research data. \n\n Learning about advanced language features (recursion\, complex data structures\, comprehensions\, exceptions) that are relevant to bioinformatics work\n Learning about development tools (benchmarking\, profiling\, unit testing) that can make it easier for us to write code that is both *fast* and *correct*\n Learning about different programming styles and concepts (object-oriented programming\, functional programming) that are suitable for different kinds of problems.\n\n\nThe workshop will use examples and exercises drawn from various aspects of bioinformatics work. After completing the workshop\, students should be in a position to (1) take advantage of the advanced language features in their own programs and (2) use appropriate tools when developing software programs. They will also have a deeper understanding of how Python works internally\, which will be invaluable when making sense of existing code and packages. \n \n \n \n \n \n Intended Audiences\n This course is designed for people who already know some Python and who are interested in tackling more ambitious programs\, particularly ones that will deal with large or complex datasets and will therefore need to work efficiently. Students should have a basic biological background (or be prepared to ask a lot of questions!) as the examples and exercises assume some knowledge of what DNA is\, what is meant by gene expression\, how to read a phylogenetic tree\, etc. \nThIS course is **NOT** suitable for complete beginners to Python as we will assume quite a lot ofknowledge of the basic syntax of the language. The material covered in the Introduction to Python for Biologists course is a suitable background. If you are not sure about whether the course is suitable for you please  email martin@pythonforbiologists.com and I will help you figure it out. \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time zone – UK (GMT+1) local time \nAvailability – 20 \nDuration – 4 days\, 8 hours per day \nContact hours – Approx. 28 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n Lectures/discussions of Python code\, libraries and techniques delivered using interactivenotebooks. Workshop/practical time for students to tackle carefully designed programmingchallenges that use the material from the discussion sessions. Usually followed up bydiscussion of solutions\, wrap up and summarisation. \n \n \n \n \n \n Assumed quantative knowledge\n Only descriptive statistics; the sections on performance require us to be comfortable discussing means and standard deviations. \n \n \n \n \n \n Assumed computer background\n This course assumes a background knowledge of Python syntax\, so is not suitable for complete beginners to programming. If you have any questions about whether the course is suitable\, don’t hesitate to email martin@pythonforbiologists.com to chat. \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of Python is required. Python is free and open-source software for PCs\, Macs\, and Linux computers.\n \nParticipants should be able to install additional software on their computers during the course (please ensure you have administration rights to your computer).\n\nAlthough not absolutely necessary\, a large monitor and a second screen could improve the learning experience. Participants are also encouraged to keep their webcams active to increase their interaction with the instructor and other students. \nDownload Python \n  \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 \nPLEASE READ – CANCELLATION POLICY \n\n\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 \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 28th\n Day 1  – Classes form 09:30 – 17:30 \nSession 1 : Recursion and trees \n\nIn this session we will cover two very closely related concepts: trees (i.e. the various ways that we can store hierarchical data) and recursive functions (the best way to operate on treelike data). As recursion is inherently confusing\, ww will start with a gentle introduction using biological examples before moving on to consider a number of core tree algorithms concerning parents\, children\, and common ancestors. In the practical session we'll look in detail at one particular way of identifying the last common ancestor of a group of nodes\, which will give us an opportunity to explore the role of recursion. Core concepts introduced: nested lists\, storing hierarchical data\, recursive functions\, relationship between recursion and iteration.\n\nSession 2 : Complex data structures \n\nIn this session we will briefly recap Python’s basic data structures\, before looking at a couple of new data types — tuples and sets — and discussing where each should be used. We will then see how we can combine these basic types to make more complex data structures for solving specific problems. We will finish our discussion by looking at specialized data types that are found in the Python core library. This session will also be our first introduction to benchmarking as we talk about the relative performance of different data types. In the practical session we will learn how to parse an input file into a complex data structure which we can then use to rapidly query the data. Core concepts introduced: tuples\, sets\, higher-order data structures\, default dicts\, Counters\, big-O notation.\n\n \n \n \n \n \n Wednesday 29th\n Day 2  – Classes form 09:30 – 17:30 \nSession 3 : Classes and objects \n\nIn this session we will introduce the core concepts of object-oriented programming\, and see how the data types that we use all the time in Python are actually examples of classes. We'll take a very simple example and use it to examine how we can construct our own classes\, moving from an imperative style of programming to an object-oriented style. As we do so\, we'll discuss where and when object-orientation is a good idea. In the practical we will practise writing classes to solve simple biological problems and familiarize ourselves with the division of code into library and client that object-oriented programming demands. Core concepts introduced: classes\, instances\, methods vs. functions\, self\, constructors\, magic methods.\n\nSession 4 : Object-oriented programming \n\nFollowing on from the previous session\, we will go over some advanced ideas that are common to most object-oriented programming languages. For each idea we'll discuss the basic concept\, the scenarios in which it's useful\, and the details of how it works in Python. This overview will also allow us to consider the challenges involved in designing object-oriented code. In the practical we will work on a simulation which will involve multiple classes working together. Core concepts introduced: inheritance and class hierarchies\, method overriding\, superclasses and subclasses\, polymorphism\, composition\, multiple inheritance.\n\n \n \n \n \n \n Thursday 30th\n Day 3 – Classes from 09:30 – 17:30 \nSession 5 : Functional programming in Python \n\nThis session will start with a look at a few different concepts that are important in functionalprogramming\, culminating in a discussion of the idea of state and its role in program design. We will see how functional programming is\, in many ways\, the complement of object-oriented programming and how that realization informs our decision about when to use each approach. We will take a quick tour of Python's built in tools that take advantage of functional programming and see how we can build our own. We will finish with a brief look at how functional programming can vastly simplify the writing of parallel code. In the practical\, we will practise using Python's built in functional tools\, then implement one of our own. Core concepts introduced: state and mutability\, side effects\, first-class functions\, declarative programming\, lazy evaluation\, parallelism\, higher-order functions.\n\nSession 6 : Exception handling \n\nThis session will start with a reminder of the difference between syntax errors and exceptions\, after which we will explore the syntax involved in catching and handling exceptions. We will then examine the way that exceptions can be handled in multiple places and the consequences for program design. We will finish this session by learning how we can take advantage of Python’s built in exception types to signal problems in our own code\, and how we can create custom exception types to deal with specific issues. In the practical we will modify existing code to make use of exceptions. Core concepts introduced: exception classes\, try/except/else/finally blocks\, context managers\, exception bubbling\, defining and raising exceptions.\n\n \n \n \n \n \n Friday 31st\n Day 4 – Classes form  09:30 – 17:30 \nSession 7 : Performance optimization \n\nIn this session we will learn about the various tools Python has for benchmarking code (i.e. measuring its memory and runtime performance) and for profiling code (identifying areas where improvements can be made). We will see that different tools are useful in different scenarios\, and collect a set of recommendations for improving program performance. We will use these tools to illustrate and measure points about performance that have been made through the course. In the practical\, we will take real-life code examples\, measure their performance\, and try to improve it. Core concepts introduced: function profiling\, line profiling\, profiler overhead\, timing.\n\nSession 8 : Unit testing \n\nIn this session we will begin with a gentle introduction to testing which will illustrate why it is useful and what type of problems it can solve. We will run through a series of examples using Python’s built in testing tools which will cover a number of different testing scenarios. We will then implement the same set of tests using the pytest testing framework and examine how using a framework makes the tests easier to write and interpret. After looking at a number of specialized tests for different types of code\, we will discuss the impact of program design on testing. In the practical we will practise building and running test suites for existing code.\n\n\n \n \n \n \n \n \n \n \n Dr. Martin Jones\n \n Martin a freelance trainer specialising in teaching programming (mostly Python) and Linux skills to researchers in the field of biology. He trained as a biologist and completed his PhD in large-scale phylogenetics in 2007\, then held a number of academic positions at the University of Edinburgh ending in a two year stint as Lecturer in Bioinformatics. I launched Python for Biologists in 2015 and have been teaching and writing full-time ever since. URL:https://prstats.org/course/advanced-python-for-biologists-apyb01/ 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/2025/04/AYPB01.jpg GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260323 DTEND;VALUE=DATE:20260328 DTSTAMP:20260417T030310 CREATED:20260112T120954Z LAST-MODIFIED:20260413T143257Z UID:10000578-1774224000-1774655999@prstats.org SUMMARY:Causal Inference for Ecologists (CIFE01) 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/causal-inference-for-ecologists-cife01/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.org/wp-content/uploads/2026/01/CIFE01-1.png GEO:53.1423672;-7.6920536 END:VEVENT END:VCALENDAR