Periodic reminder to check out my new webcomic, Remarkable Doodles!

Title: If Isaac Newton has seen further

Permalink: pminasandra.github.io/remarkable/?...

I have a new webcomic! pminasandra.github.io/remarkable/?...

Although I use Bluesky mainly for science, I think some of you might enjoy some of these more nerdy comics. Some examples uploaded below. Check out the link for more!

🧠 What does collective movement look like in cognitively advanced animals?

I have a new pre-print out for a super short solo-author paper I just submitted! I explore how the ability to forecast others' behaviours affects your own decision-making. Check it out: doi.org/10.1101/2025.08.14.670290

Remarkably, predictivity decay was quantitatively similar across all studied species and individuals, hinting at *general principles* underlying animal behaviour. (9/10)

We also explored how current behaviour predicts future behaviour at various timescales—something we call "predictivity decay."
Predictivity decay quantifies how quickly we lose predictive power when forecasting an animal's behaviour due to the accumulation of stochasticity. (8/10)

Surprisingly, across states, individuals, and species, we found the same result: the longer an animal continues a behaviour, the **LESS** likely 📉 it becomes to switch away in the next instant. (7/10)

Imagine a hyena that's been walking for 10 minutes. As it keeps walking, with time does the probability that it switches its behaviour increase or decrease? What about this probability in other behavioural states, do you think it goes up or down? (6/10)

Our study animals live in different habitats & behave differently based on context and goals, so we didn't expect universal patterns in how they switch between behavioural states. However, we found them anyway! (5/10)

Through extensive fieldwork, multi-sensor collars with accelerometers, and machine learning, we arrived at behavioural sequences of multiple animals from our three different species. The behavioural sequences were several days - several weeks long. (4/10)

All animals behave. To quantify underlying rules of behaviour, we need behavioural sequence data. For simplicity, think of behaviour as sequences of states. E.g., a meerkat lies down (600s), stands up (20s), then moves searching for food (450s). (3/10)

🚨 Out this week in @pnas.org 🚨
The flagship paper from my PhD @mpi-animalbehav.bsky.social @livingingroups.bsky.social - We show surprising statistical similarities in animal behaviour across states, individuals, and even species.
www.pnas.org/doi/10.1073/...
(🧵 1/10)

My favourite part of working on selfish-herd style models of animal movement is these very pretty plots