Introduction to structured population models and demographic distribution models (IIPM01)
18 November 2019 - 22 November 2019£500
Structured population models have become a central tool to explore evolutionary and ecological processes, such as selection gradients or responses to climate change, based on field demographic data. With recent advances in theoretical ecology and computing power, these models have further evolved. Over the last decade, Integral Projection Models (IPMs), a generalization of Matrix Populations Models, have gained popularity because of their simplicity, robustness, and flexibility. This course will draw on the connections between these approaches to help researchers understand and predict how individual performance scales to population-level dynamics. The course will cover all aspects of analysis, including data preparation, regressions, matrix/intregral projection modeling, and sythesis for understanding population structure. Special enphasis with be places on demogrpahically-driven distribution models. Participants are encouraged to bring their own data to analyze, however example data sets will be provided for model exploration. Each day, we’ll 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 a mechanistic understanding of population dynamics and patterns that emerge from those dyanamics.
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.
To book ‘COURSE ONLY’ please scroll to the bottom of this page.
Other payment options are available please email email@example.com
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
Basic familiarity with regression is essential. Experience with linear algebra and Bayesian methods is helpful, but introductions will be provided.
Assumed computer background
Basic 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.
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK email@example.com
Monday 18th – Classes from 09:00 to 17:00
Day 1 begins with a background on the similarities and differences between the two primary types of structured populations models and methods for calculating common population statistics.
1) A background in Matrix Population Models
2) Extensions to Integral Population Models, and vital rate regressions
3) Population statistics – lambda, elasticity, sensitivity, and more
Tuesday 19th – Classes from 09:00 to 17:00
Day 2, we’ll look at some of the challenges of modeling vital rates for different life histories and some common pitfalls. We’ll explore practical challenges with some detailed examples.
4) Individual growth, stasis, and shrinkage
6) Fecundity is complicated
7) Example: an endangered overstory shrub
8) Example: an invasive biennial herb
Wednesday 20th – Classes from 09:00 to 17:00
Day 3 will focus on checking and validating IPMs from both a statistical and biological perspective. We’ll extend IPMs coupled with environmental data to build dynamic species distribution models.
9) Model diagnostics
10) Improving IPMs – how do vital rate regressions influence population statistics?
11) Demographic distribution models
Thursday 21st – Classes from 09:00 to 17:00
Day 4 will focus on producing measures of timing, individuals classified by multiple variables, and special challenges for organisms that live much longer than the study period.
12) Age from stage statistics
13) Individuals characterised by multiple states (e.g., age and stage)
14) Tree Demography
Friday 22nd – Classes from 09:00 to 16:00
Day 5 will conclude with some mode advanced statistical methods to incorporate uncertainty in predictions and infer or working around data missing from a species life history.
15) Bayesian methods – capturing uncertainty
16) Inferring missing life history information
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)