Advanced Range, Niche, and Distribution Modeling (ASDM01)
25 November 2019 - 29 November 2019£550.00
Range modeling, also known as niche, distribution, or habitat use modeling, is among the most widely used tools in ecology. Its popularity is evidenced by the wide range of conceptual and statistical approaches that have been developed over the last two decades for quantifying ranges. We will spend much of our time on the most popular approach to range modeling – presence-only models – however we will draw connections to other data types and general principles for any range model throughout. We will emphasize understanding how modeling decisions affect predictions and identify whether biology or statistics can provide insights into those decisions. By surveying a broad spectrum of approaches, you’ll learn how to find the right tool for the job and understand the pros and cons of each. Each day we will reserve time for open work sessions where students can receive mentoring while applying new skills to their own data sets or example data sets provided by the instructors.
Researchers interested in advancing their range modeling and statistical skills with a comprehensive survey of available tools in biogeography and habit use.
Venue – Pasteur Hellenic Institute, Athens, Greece – Google map
Availability – 20 places
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English
We only offer a COURSE ONLY package for this course.
• COURSE ONLY – Includes lunch and refreshments.
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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 firstname.lastname@example.org Failure to attend will result in the full cost of the course being charged. In the unfortunate event that PRstatistics must cancel this course due to unforeseen circumstances a full refund for the course will be credited. However PRstatistics cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.
Dr Corey Merow
There will be morning lectures based on the modules outlined in the course timetable. In the afternoon there will be practicals based on the topics covered that morning. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.
Assumed quantitative knowledge
Familiarity with one or more common approaches to modeling distributions is expected; these might include using Maxent, biomod2, or GLMs. Some familiarity iwth Biasyesian principles will be helpful, although we’ll include a brief refresher.
Assumed computer background
Experience with R, including regressions, graphics, and manipulating data frames. This experience often corresponds to one or more years of using R regularly.
Equipment and software requirements
A laptop/personal computer with a working version or R and RStudio installed. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links.
It is essential that you come with all necessary software and packages already installed (you will be sent a list of packages prior to the course) internet access may not always be available.
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Monday 25th – Classes from 09:00 to 17:00
Day 1 begins with a background on the most commons information used for range modeling, exploratory analysis, and identifying the similaries, differences, and subtleties of each.
1) Data types and their strenths and weaknesses
2) Roles for simplicity and complexity in model design
3) Presence-only models: Connections between Maxent, Point process models and GLMs
4) Coding tips and designing a reproducible workflow
Tuesday 19th – Classes from 09:00 to 17:00
Day 2, As models using only presence data are by far the most common, webll spend some time investigating the subtleties of different modeling challenges, and the connections between modeling decisions and predictions.
5) Advances in presence-only modeling
6) Modeling sampling bias – the key to robust obtaining models
7) Designing block cross validation
8) A wide range of options for evaluating model performance
9) Thresholding predictions – do you have to?
Wednesday 20th – Classes from 09:00 to 17:00
Day 3 will focus on advanced challenges with presence-only models.
10) Extrapolation and transfering predictions to new times or locations
11) Integrating presence-only data with other data types
12) Spatial prior information – expert maps, dispersal models, related species
13) Spatially explicit models
Thursday 21st – Classes from 09:00 to 17:00
Day 4 will focus on different modeling approaches that are appropriate with different data types. * Occupancy models and detection bias.
14) Emerging algorithms, especially for small sample sizes
15) Machine learning algorithms what’s under the hood, and does it matter?
Friday 22nd – Classes from 09:00 to 16:00
Day 5 will conclude with a survey of the next generation of range modeling tools including data fusion approaches and provide ample time for students to receive advice while working with their own data sets.
16) Data fusion models
17) Joint distribution models
18) Masking distributions – when do we really need statistics?
The instructors were excellent and clearly were the reasons for my previous comments. They both combined a deep understanding of statistics and ecology at the same level.Any questions or queries I’ve had, were thus first answered with an ecological point of view and then translated into statistical consideration thereby making much more sense on both side.In addition the course was very well organised, the course director and the two instructors were very friendly as well as professional. On the top of learning many useful things, I’ve also had a very good time during the week there.” Clement Garcia,
Spatial ecologist, Centre For Environment, Fisheries & Aquaculture Science (CEFAS), England
(Attended ADVR course)