Insights into Complex Group Behavior in Animals

Collective animal behavior is a curious phenomenon in the animal kingdom. Animals of varying complexity and intelligence have shown the capacity to move in highly coordinated groups. This includes flocks of birds and foraging parties in ant colonies. Scientists haven’t been able to figure out exactly how these complex groups function. However, recent experiments have generated insight into some key mechanisms underlying this behavior.

Researchers wanted to better understand the flocking behavior of jackdaws, birds that closely resemble crows in physicality and intelligence. They began tracking the birds flight patterns using high-speed cameras to create three-dimensional maps that show the flight path of every bird in the flock. In the winter months after foraging for food, the birds will travel together back to their nests; scientist call this a transiting flock. To test how their group behavior changed given different environmental conditions, researchers placed a robotic fox in a field near the bird’s nests. The fox appeared to be eating a nearly dead jackdaw, and scientists played recorded sounds of jackdaws screeching at a predator to make the scene more realistic. They noticed that the transiting flock immediately changed their flight pattern and overall group structure as they mobbed the fox, maintaining distance to keep themselves safe. But how did they communicate this complicated attack strategy so quickly?

Jackdaws are highly intelligent social animals known for their practice of food sharing between kin.  

In transiting flocks, the jackdaws tend to receive information about where to move from other birds in a fixed distance around them. These types of interactions are called metric interactions. In this mobbing flock, birds seemed to be receiving info from specific birds in the group. These are known as topological interactions. Previous research had demonstrated that the migrating flock structure was determined by topological interactions, effectively following the leader of the pack home. However, the mobbing behavior observed reflected more metric interactions. The scientists believe that the birds communicated either visually or through auditory cues to transition from topological interactions to metric interactions but they still aren't sure how they do it. 

As we've seen so far, complicated group behavior seems to rely on a considerable amount of intelligence. However, even the smallest ant is capable of this feat. Ants tend to organize themselves in social structures called ant colonies. Ant colonies share information about food availability and coordinate foraging parties to search for and collect food for the group. For example, turtle ants, black ants that have a unique dish-like head, send out foragers to search for food. Ants in the foraging party communicate by leaving a trail of pheromones on their way to a food source. In the case that their path is blocked or broken due to some environmental event, the ants have learned to change their path to access the food source. 

Turtle ants elect a single queen per colony. The picture above is a queen ant from the turtle ant species Cephalotes atratus.

Researchers have been able to identify several algorithms that the ants may be using to augment their trail. For example, they modeled the ants path from their colony to the food source as a graph, or a collection of nodes and edges that connect the nodes together. In this model, nodes represent breaks in the vegetation and paths represent trails with varying amounts of pheromones'. The ants then choose a path based on the amount of pheromone on the trail. Occasionally, a some pheromone is artificially added to a less ideal path to allow for exploration. While it’s not the most efficient algorithm, they were able to show that it was effective in different environmental conditions. 

Biologists study group behavior to learn more about specific species of animals. However, the methodology behind these coordinated groups can be used in other fields such as engineering to create robots with swarming behavior. These complicated systems can be made from simpler units that follow a set of simple behavioral rules, like metric interactions. In addition, insights from how ant colonies adapt to their environment can be used to develop simple systems that can respond flexibly to changing conditions. 

Here are some cool videos of the 3-D flight paths in the Jackdaws experiment: