When to Call in the Posse: Ecology answers when to hunt (or shop) with others, and when to go it alone
Fisherman are notorious hoarders of secrets. Your grandfather’s favorite fishing hole is information that might be shared only after you’ve attained an age at which such a precious secret can be entrusted. If it’s possible to be more stingy with locations, mushroom hunters are perhaps even more secretive about their favorite morel or chanterelle patches. Whether or not to share information while foraging is also important to animals, and it turns out that the same principles that govern their decisions may apply equally to fishing, mushrooming, shopping, dating, or economics according to a new paper in PLOS Computational Biology.
In fact, I think you can optimize your Cyber Monday shopping strategy using these findings…and by the end of the article I think you’ll agree
Ants and bees broadcast the fact that they’ve found a food source, attracting others to the location. Wolves hunt in coordinated packs, whales in well-organized pods. In contrast, sharks are solitary. In between the two is a myriad of different stratagies, with some animals sharing food information with others to vary degrees. Monkeys might alert the rest of the troop to the presence of food sometimes, but other times they might keep that knowledge to themselves. What governs when to share foraging information and when to keep it to ourselves?
When I was a kid everybody shared which houses gave out the most candy at Halloween…but once I got home I would hide my candy so nobody else could eat it. I’m more than willing to share my fishing spots with people in person, but I’m unlikely to post details to Facebook. We all share some information about our foraging, and withhold other information. What drives that decision?
The degree to which animals share foraging information is clearly connected to optimal foraging theory, which we already know is related to much more than just animals finding food (remember, you are an information wolverine). There is reason to think that this sharing of information is important in the wild though. If a lion encounters a water buffalo it makes sense to bring in the rest of the pride, she can’t bring it down by herself. But when that same lion comes across an injured gazelle it makes more sense to simply do the job alone. But at what point is the prey big enough to be worth bringing in the pride? A bee that encounters a new food source clearly has incentive to share the news, there is more nectar in a field than one bee could ever collect. But what constitutes a new enough, or big enough, food source to elicit a dance?
The decision point of when to involve others in foraging could affect the health of the pride or the hive, but more importantly, over generations, the decisions that individuals make may determine whether animals tend to forage together (pack animals) or separately (solo hunters like great white sharks).
There are six main steps: (I) predators rapidly traverse the domain by moving in a straight line; (II) they randomly stop to sense their local environment, and pick a new direction if no prey patch is found within their sensory radius; (III) if a prey patch is found, the predator moves towards its centre and consumes prey from it; (IV) if a predator has a social tie with another predator who is at a prey patch, then information may be passed between the two, (V) informed predators move towards the nearest prey patch whose location they know; (VI) prey patches make random jumps at a given rate.
The new research by Mathieu Barbier and James R. Watson attempts to provide a basic mathematical explanation for this information sharing among foraging animals. Their approach is purely mathematical and theoretical, but the ideas they use to build the model are pretty common sense. They assume that animals forage at some speed, that they stop to sense the environment for prey at some interval, and that they can communicate at some maximum distance once they find prey. They also assume that the landscape changes (or the prey moves) at a certain rate, and that prey occurs in various sized patches (this could mean literally the size of a patch of vegetation, or it could mean the size of an individual animal, or the size of a herd of animals).
Their model is pretty simple and open ended, and that is the intent. By laying out the very basics the model can be fit to many different situations that share these basic qualities, and added to for specific situations where we know more detailed information about the specific case.
So…what did they find?
Who should you share your favorite mushroom patch with? Should you share that Cyber Monday sale with everyone you know, or should you keep it to yourself?
The article concludes that it all depends on how long it takes to find and eat the “prey” you are looking for, how long it takes the “prey” to get-outta-Dodge, and how much of it there is to go around?
If the search time is high, then prey is takes a long time to find. The amount of food is described in terms of the “handling time”. In other words, the time it would take to eat it all. The speed of the prey (or the speed that the landscape changes if you are talking about, say, a patch of grass) is called the “landscape time” in the model.
The authors find that if there isn’t much food to go around then it isn’t worth it to call in everyone you know. So, for a wolf who just found a mouse it doesn’t make sense to get the whole pack involved…just snarf the bugger down for a quick snack. But, if it took a long time to find that food and it would take awhile to eat that food (say, an elk)…and especially if that food might run away fast (elk are crazy fast)…then it makes sense to call in the pack and make a big meal of it.
What does this have to do with Cyber Monday you ask?
Well, imagine that the “search time” in the plot to the right represents how long it took you to find that amazing sale, and “landscape time” is how long the sale will last. Finally, the “handling time” (how long it takes the wolf to eat that elk) is how many items are available. Lets imagine two different scenarios here, one with a popular item on Amazon.com and another in a boutique store on Etsy. In this case the colors on the plot indicate whether to tell more people (RED) or keep the deal to yourself (BLUE).
For a popular item on Amazon the search time should be small, Amazon has great search tools and their popular sale items are well publicized. The “landscape time” is short, this is a one-day sale after all. We don’t really know the “handling time” because we don’t know when the item might sell out, but Amazon has warehouses full of stuff, so it should take a decent amount of time for the items to sell out. In this case, looking at the heatmap plots from the paper and imagining that the axes represent our Amazon sale item (negative landscape time, positive handling time, positive search time) the model says that it’s probably fine to let your friends in on the deal.
But, imagine that instead of Amazon we’re talking about an obscure Etsy shop with limited stock. It took you awhile to search for the deal on the site because the shop doesn’t have millions to spend on advertising. Again, this is a one-day Cyber Monday sale so the landscape it changing fast. Also, this shop doesn’t have much stock, so it won’t take long to sell out, so the “handling time” is really low. In that case the plot is clear…don’t tell ANYONE about this deal until you’ve ordered for yourself.
So, yet again, we’ve learned that optimal foraging theory not only applies to animals, but also to our daily lives.
The authors of the paper cite a few examples, including oil extraction and finance. They even mention that you could apply this concept to dating (should you bring a wingman to that new dive bar, or go with a group?). It doesn’t take long to imagine other ways this model could be applied.
Who knew that ecological theories could have real-world applications? But I’m still not sharing my mushroom patches, not with anybody!!