Post provided by Jordan Milner
Each year Methods in Ecology and Evolution awards the Robert May Prize to the best paper published in the journal by an author at the start of their career. Ten Early Career Researchers made the shortlist for this year’s prize, including Jordan Milner who studied for his PhD at the University of Sheffield in the UK. In this interview, Jordan shares insights on his paper ‘Modelling and inference for the movement of interacting animals’.
Tell us your career stage, what you work on, your hobbies and interests
I was studying for my PhD at the University of Sheffield whilst working on this article. During that time, I sat in both the Statistics and Mathematical Biology research groups whilst developing statistical animal movement models that can be used to study the movement patterns and behaviours of social animals. I defended my thesis towards the end of 2021 and I have since been working in industry.
Outside of ecology, my interests lie in whatever sport is being played nearby and I have recently being getting into woodworking. I am also perhaps a little overexcited at having recently acquired an allotment!
How would you pitch your article to someone if you had just 30 seconds in an elevator?
The statistical modelling of animal movement data is a rapidly growing area of research as it allows us to study animal behaviour when we cannot directly observe it. The majority of that growth has concerned the development and application of models designed to analyze the movement of an individual animal. Whilst those models are incredibly useful tools, they are not able to account for any social drivers behind an animal’s movement behaviour. The model we presented in this paper was designed to capture that missing information.
The social framework of the model is based on a dynamic social hierarchy and so we able to infer which animals are influencing a group’s movement and when. It is flexible enough to capture a wide range of social information, such as changes in leadership or in the number of subgroups.
Where did the idea to develop this method come from?
In various areas of animal behaviour/movement literature (e.g., those concerned with social network analysis, fission-fusion dynamics and other movement models), a common goal or desire is to be able to capture the dynamics of social behaviour and/or granular information such as influential animals or subgroups. Such information can be useful for population management and conservation efforts and so I wanted to develop a movement model that could provide it.
The statistical basis of the model was inspired by previous work undertaken by my co-authors.
What were the major challenges in developing this method? How did you overcome this?
Whilst I think the output of the model is simple to interpret, it is quite complicated under the hood. In particular, the movement of all of the animals being studied is jointly modelled with a multivariate distribution, a distribution that required a lot of derivation. Unfortunately, there was no easy way to circumvent that – it was just a case of putting my head down until it was done!
How do you plan to apply the method you published/what have you been working on since its publication?
The model in the article is only suited to the spatially homogeneous case. I have since been working on developing it for the spatially heterogeneous case so that we can also look at how social behaviours interplay with environmental factors.
Additionally, I have extended the model so that a “zone of interaction” can be included in the framework. That is, animals can only be inferred to interact if they are within some proximity (also inferred). This feature adds some biological realism into the model by discouraging the inference of spurious, overly-distant interaction.
Who will benefit from your method?
The model was developed with population management or conservation in mind. For instance, it can help identify influential members of a group or aid investigations into how external factors (like climate change) are impacting socials dynamics.
In terms of species, any that live in complex social groups. Though, due to the sizeable computational cost of fitting the model to data, it is currently best suited to smaller groups.
If you could travel back in time, would you add to or change anything about your method?
This article contains an early iteration of this modelling framework. As such, there is work to be done on both increasing its functionality (e.g., allowing for spatial heterogeneity) and decreasing the computational cost of fitting it to data. I had time to work on the former during the remainder of my PhD, but not the latter unfortunately.
You can read Jordan’s full paper here.
Learn more about the Robert May Prize 2022 shortlist here.