We are pleased to announce David Wilkinson as the winner of the Robert May 2021 Award, given the best work by an early career researcher in the 2021 volume Methods in Ecology and Evolution.
In this interview, David shares knowledge on his winning article “Determining and Evaluating Predictions of Common Species Distribution Models”. Congratulations to all the selected authors, whose articles you can read in our virtual issue of the Robert May 2021 Award.
Tell us about your career stage, what you do, your hobbies and interests
I am a researcher at the University of Melbourne and am almost two years after my PhD. I am sitting in two research labs at the moment: Quantitative and Applied Ecology Group (QAECO) AND MetaMelb. I conduct my ecological research at QAECO where I work on the application of species distribution patterns in a variety of contexts, most recently in response to the 2019/20 Australian fires and Arctic marine forests. At MetaMelb I lead the Data Management and Analysis team for the repliCATS project where we collect source assessments of the reliability of published science. Outside of work I like to read a lot of fantasy and science fiction, playing board games and building things. I have been building and painting sets of patterns for almost twenty years, and in recent years I have been involved in woodworking to build my furniture.
How would you submit your article to someone if you only had 30 seconds in an elevator with him?
In the wild species exist in communities and there are many benefits to modeling them as such. When we do this, several options open up as to how we can predict species distribution and community aggregation.
Where did the idea to develop this method come from?
My PhD became a deep dive into Common Species Distribution Models (JSDM) and what we could do with them. We started by comparing different JSDM implementations in a standardized framework, so that we could identify where they were the same and how they differed. Single species models are used for forecasting all the time, so it was a natural breakthrough to explore how this could be done in the context of many species.
What were the main challenges in developing this method? How did you overcome this?
The two biggest challenges here were my lack of a statistical background and how intensively computational these methods can be in larger datasets. I had not calculated an integral since high school, so that meant I had to read a lot to calculate my integral head in space with many variations. Drawing physical representations of probability distributions was a great help in understanding what the different types of prediction were doing. To be able to apply these methods to larger databases, I had to learn how to use a supercomputer, and this has definitely been one of the most useful skills I have developed throughout my doctorate.
How do you plan to apply the method you have published / what have you been working on since its publication?
We have worked on a comparison of the predictive performance of different JSDMs using the different types of forecasting we defined in this paper in several real and simulated datasets. We are approaching the finalization of the manuscript for submission, so hopefully you will hear from us very soon…
Who will benefit from your method (researchers, species, habitats, community groups)?
Anyone working with species or communities can benefit from this method. You may need multi-species data to fit an JSDM, but you can generate species or community-level forecasts so that they can be used for a variety of storage applications. These forecasts will be useful for researchers, land managers, policymakers and more.
If you could travel back in time, would you add or change anything about your method?
I would definitely like to improve the computational performance of these methods. For larger data sets, it may take days or even weeks on a supercomputer to generate forecasts and will undoubtedly be a hindrance to the wider use of the method for high-dimensional data sets.
You can read the full letter of David here.
Read the full virtual edition of the Robert May 2021 Award here.