A Minimal Introduction to Ecological Forecasting
This module is a minimal introduction to Ecological Forecasting for the 3rd year undergraduate Biological Sciences class in Quantitative Biology (BIO3019S) at the University of Cape Town. A brief introduction to the course can be found here.
I provide a very brief introduction to the framework for Ecological Forecasting. We only have a two weeks, so this really is a minimalist introduction. I’ll focus on providing a broad overview of the general framework and motivation for ecological forecasting, but won’t have time to delve into the more gory theoretical and statistical details. I mostly use Ecological Forecasting as a framework to highlight various themes and principles that are increasingly important for quantitative biologists - understanding how we inform or make decisions; managing data; working reproducibly; propagating, understanding and reducing uncertainty, etc.
Not all of this is fun and exciting, but as I said, it is important stuff for quantitative biologists to know. I’ll try my best to make it interesting! Hopefully by the end of the module you’ll see the value in it all - both for you as an individual and for science and society in general.
The core outcomes/concepts I hope you’ll come away with:
- To be able to situate the role of models and the importance of forecasting in science and ecological decision making
- Familiarity with the concepts and understand the need for Open, Reproducible Science
- Familiarity with The Data Life Cycle
- Familiarity with the value and flexibility of Bayesian statistical methods
- Some familiarity with sources of uncertainty and the need to characterize, propagate, analyze, reduce and present uncertainties when forecasting
- See additional revision questions in section 12.4.
Lectures will be held live 12:00 - 12:45 from 24 July - 4 August.
I’ll be refining and adding to (and mostly teaching from) these online course notes as we go.
There is only one practical for this module, 2-5PM on Tuesday the 1st August. You will need to spend half an hour setting up the required software on your laptops before Monday the 31st July!!! See instructions below.
Your report on the practical will be due on Tuesday, 8th August - You will be evaluated on how well you completed the Github task during the practical and your answers to a short set of questions about the analyses. Answering the questions shouldn’t take more than half an hour.
You need to install and set up RStudio and Github and test your setup. You can find the 11-step instructions in section 2. This may be a bit tedious, but there’s no other option really. I’ve done my best to make it as painless as possible. It should take you about half an hour if all goes well… (less if you have R and RStudio installed already).
PLEASE DO THE SETUP BEFORE WE MEET!!! I will check in on Monday the 31st July to see if people are having issues, but don’t expect my help if you haven’t tried by yourself first. Trust me, I will be able to tell…
- You are expected to read Dietze et al. (2018) for Tuesday the 1st August. You can download it here. You will need to answer a few questions beforehand on this Google Form.
The following 4 minute video will give you a glossy overview of what most of this module is about:
I’m an ecologist who has become more quantitative through time, but has little formal training in quantitative methods (i.e. I’ve learnt by doing over the past 20 years). As such, I still make elementary mistakes. In fact, the entry requirements for this course are beyond my formal training, so you may have much to teach me! If you spot any errors, confusion or contradictions, please let me know and I’ll get back to you and/or update the course notes accordingly. Hopefully by the end of the course you can suggest changes directly using pull requests to the GitHub repository for the course notes.
The following resources were instrumental in me pulling this material together and are worth spending time exploring. I cite my sources throughout the course notes, so check out the references at the end of each section and the end of the course notes for more.
- Dietze, Michael C. 2017. Ecological Forecasting. Princeton University Press. https://doi.org/10.2307/j.ctvc7796h.
- Dietze, Michael C. et al. 2018. “Iterative near-Term Ecological Forecasting: Needs, Opportunities, and Challenges.” Proceedings of the National Academy of Sciences of the United States of America 115 (7): 1424–32. https://doi.org/10.1073/pnas.1710231115.
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