From swarm to school, stickleback groups differ repeatedly in their collective performance
among schooling fish, groups can have different collective personalities, with some shoals sticking closer together, being better coordinated, and showing clearer leadership than others.
For centuries, scientists and non-scientists alike have been fascinated by the beautiful and often complex collective behaviour of animal groups, such as the highly synchronised movements of flocks of birds and schools of fish. Often, those spectacular collective patterns emerge from individual group members using simple rules in their interactions, without requiring global knowledge of their group.
In recent years it has also become apparent that, across the animal kingdom, individual animals often differ considerably and consistently in their behaviour, with some individuals being bolder, more active, or more social than others.
New research conducted at the University of Cambridge’s Department of Zoology suggests that observations of different groups of schooling fish could provide important insights into how the make-up of groups can drive collective behaviour and performance.
In the study, published today in the journal Proceedings of the Royal Society B, the researchers created random groups of wild-caught stickleback fish and subjected them repeatedly to a range of environments that included open spaces, plant cover, and patches of food.
My latest paper on the collective behaviour of stickleback shoals is out today in the journal Current Biology!
Jolles, JW, Boogert, NJ, Sridhar, VH, Couzin, ID, Manica, A. (2017) Consistent individual differences drive collective behaviour and group functioning of schooling fish. Current Biology 27: 1-7. doi: 10.1016/j.cub.2017.08.004 (link).
Highly coordinated school of three-spined sticklebacks swimming in the blue waters of the Bodensee near Konstanz, Southern Germany. Photo: Jolle W. Jolles
New research sheds light on how “animal personalities” – inter-individual differences in animal behaviour – can drive the collective behaviour and functioning of animal groups such as schools of fish, including their cohesion, leadership, movement dynamics, and group performance. These research findings from the University of Konstanz, the Max Planck Institute of Ornithology and the University of Cambridge provide important new insights that could help explain and predict the emergence of complex collective behavioural patterns across social and ecological scales, with implications for conservation and fisheries and potentially creating bio-inspired robot swarms. It may even help us understand human society and team performance. The study is published in the 7 September 2017 issue of Current Biology.
Last week I was in Catalunya visiting friends and family and some undistracted paper writing. Catalunya, where my wife grew up, is an amazing place and feels like a second home to me. With the Mediterranean sea and the Pyrenean mountains within half an hour’s drive, there is always a lot to explore.
Hiking up the beautiful Gorge of Sadernes, Catalunya.
During some recent trips, I went hiking in the Pyrenean foothills and discovered schools of Mediterranean barbel (Barbus meridionalis). They seemed to be separate populations living in semi-isolated pools of a small mountain river. This species of Barbus is only native to a small area in and around the Eastern Pyrenees. Sadly, in recent years its numbers have plummeted with 30% (source: IUCN), highlighting an urgent need to better understand their ecology and vulnerabilities.
A shoal of Mediterranean barbel foraging on limestone rocks.
The last few months I have been working hard on the sophisticated new experimental set-ups in the lab with which we will be able to get high spatial and temporal resolution tracking of large schools of fish, in tanks that are up to 3x3m in size!
To get highly accurate spatial data of the fish we need to correct for the distortion of the camera lens, which almost all lenses have to some extent. I just finished the script (in Python) that enables us to undistort the image from a camera using functions in opencv based on a video of a moving checkerboard.
Me calibrating a camera with a checkerboard pattern, with colours showing the output of my python script, with a school of 1000 moderlieschen in the background :)
It works pretty well already, even with non-optimal videos. Next step will be to stitch the videos of multiple linked camera’s.
My research is currently centred around understanding the role of consistent behavioural differences in the collective movements and functioning of animal groups. In particular, I assay large numbers of stickleback fish on various personality traits and expose them in groups to different ecological scenario’s. I have written custom tracking software in Python using the OpenCV library to be able to accurately track the position of individual fish in the freely-moving schools.
Today I wanted to share a simple visual that highlights the detailed individual-based tracking of a small fish school over time. Each fish is represented by a different colour, with the arrow showing its vectorized movement, with larger arrows indicating a higher speed. The video is centred around the vector of the group as a whole to better visualize the structure of the group over time. Lines indicate the smallest polygon encompassing all individuals and Individual Centre Distances. The moving axes indicate the relative speed of the group in a large circular arena.
In this short section of a 30-min long experimental trial it is clear that the group speed, cohesion, and structure fluctuate over time. At the same time, individuals also maintain to some extent their positions relative to the group centre, such as the green and yellow individual clearly having a stronger pulling power on the movements of the group as a whole.
I used RaspberryPi computers to film the fish, custom Python tracking scripts to acquire individual X,Y coordinates for each individual in the group, R to process the tracking data and acquire movement characteristics, and R with ffmpeg to create the visual.