In a classic The Far Side cartoon by Gary Larson, a group of cows stand on two legs chatting by the side of a road when a lookout shouts “car”. The cows immediately drop to a four legged stance as the car passes by. When it has gone, they return to their usual position and continue chatting.

The social lives of cows are clearly more complex than biologists imagine. But any mystery is about to be cleared up thanks to the work of Shi Chen at the University of Tennessee, Knoxville, and a few pals. These guys have studied the social networks formed by cattle in unprecedented detail for the first time. The results have important applications for everything from animal behaviour to disease transmission.

These guys analyse the behaviour of around 70 Holstein-Friesian calves divided between three pens, each about the size of a tennis court. Each calf had an RFID tag attached to its ear and this allowed the team to track its position with centimetre resolution.

The team judged an animal to have social contact with another if they come within 30 centimetres of each other. They measured the position of every animal continuously for a week during August in 2011. That produced almost 9000 data points for each animal.

The results provide one of the most detailed insights into the social network associated with animals ever compiled.

One possibility is that the social contacts between cows are entirely random. But the results show something else entirely. The cattle social network changes substantially both in time and space. “These results show that cattle social network density changes significantly within a day, and feeding activity promotes clustering,” say Chen and co.

In particular, about 60 per cent of the total contacts occur during feeding which takes up just 8 per cent of the total daily time. The calves have the strongest social contacts while feeding on hay rather than on grain. This is probably because cattle spend longer feeding on hay to re-ruminate having eaten grain. Curiously, drinking does not seem to be a factor in changes in the network.

One interesting point is that a certain proportion of the social contacts are random. For example, the calves may pass within 30 centimetres of each other as they cross the pen. “Thus, we argue that some of the contacts during non-feeding time are not real social interactions but perhaps just random contacts,” say Chen and co.

And during feeding time the cattle compete with each other for food at the grain bunk and therefore cannot always eat with an intentionally chosen partner. So the contacts around the grain bank may not necessarily reflect social ties.

However, after feeding on grain there is less competition and the cattle can go with a chosen partner to the hay. “It is only the contacts around the hay bunk during feeding time that may attribute to the real social ties,” they conclude.

That should have significant implications for the way animal behavioural specialists study social networks of other animals, particularly in the wild. The structure that Chen and co find in this network can only be revealed with high resolution data. They point out that the network details remain hidden if the data points are taken just once a day or have a lower spatial resolution.

What’s more, the key finding is that it is important to distinguish between random contacts and social ones but this can only be done with the aid of detailed knowledge of the animal habitat and behaviour.

Perhaps the most important application of this kind of work is the ability to model the transmission of disease through an animal social network. Until now, this has usually been done by simulating how the disease transmits from one animal to another. This latest work shows that the network allows multiple pathways to be set up simultaneously. So future simulations should be to take this into account.

That’s interesting stuff. Although it leaves one question, that Larson might ask, unanswered: what do the cows do when their RFID tags have been removed and nobody is watching?

Ref: arxiv.org/abs/1407.6074 : Spatial-Temporal Dynamics of High-Resolution Animal Social Networks: What Can We Learn from Domestic Animals?