by Aditi Mishra – With grocery shops bursting with sugar, fats and savoury snacks, finding food might not seem that hard. In fact, those prescribed a diet might even wish that it were harder. But things are a little different in the wild. Food is very hard to find and finding food is one of the most defining motivations of most animal lives.
But despite such huge differences, one thing is just the same. Be it a supermarket or the wild, food always comes with a price tag. Whether wild or civilised, everyone must pay for what they like. And where there is a price, the budget book is not far left behind.
Lagging a little behind that book are the ecologists trying to decode it.
Optimal foraging theory (OFT) is one such attempt to decode the cost of find food. It is a behavioural ecology model used routinely by scientists to predict how an animal behaves when it is searching for food. It’s a fairly simple concept. Food is obviously the prize, and scientists theorize that in order to increase fitness animals should try to forage in a way that lowers the costs associated with their food.
Now that we understand that there are costs associated with food, what do these costs look like?
Usually supermarkets are quiet affairs with their relatively tame transactions using money. But the wild world is a bit different. In the wild, quite often the food fights back. Hence, every meal could turn into a risky affair, potentially paid for in personal safety.
We may joke about an expensive bottle of wine costing us an arm and a leg, but it literally doesn’t – at least not without making news. But ask any lion who has gone after a gazelle and they would confess how dangerous a midnight snack could potentially get. This likelihood of injury and exhaustion is what ecologists like to call handling costs. It is one of the prices that you pay in terms of the risks incurred when you pursue a meal in the wild.
But loss of limb is not the only worry plaguing the animals in the wild.
Successful animals in their natural habitat must optimise for time and effort as well. After all, it takes time and effort to find food when your calories are as thinly spread out as they are in the wild. Be it a grazer who has to cover large ground to have their fill, or a hunter that can use only a few hours in dim moonlight – time is of the essence. Plus, at the end of the day you can only be at one place at one time. If you spend all your time at a less than favourable food source, you cannot take a bite off the juicy pastures. This investment of time and effort into one’s food is called opportunity cost.
The optimal foraging theory predicts that once costs are greater than the benefits, the foragers must change their behaviour. We see echoes of OFT in nature, across many different groups of animals.
Oystercatchers, for example, are birds that forage on mussels by cracking open their shells. When the scientists Meire and Ervynck tried to model which mussels would be most preferred by the oystercatchers on the basis of optimal foraging theory, they found a good overlap between what the model predicted and what was seen in nature. In fact, they were accurate to the range of millimetres. Factoring in prey density and handling time, they predicted a bell curve – too small mussels are easy to crack but hardly nutritious, whereas massive mussels provide a lot of food but are hard to crack. Mussels in the mid-range size of 50-55 mm were the preferred ones.
Similar studies have shown interesting behaviours in pollinators as well. Pollinators exhibit something known as floral constancy, the behaviour where an individual pollinator shows preference for a particular kind of flower. Preferences can differ between individuals of the same species, or even the same hive. And preference can be as specific as a particular colour morph of a flowering species. Having floral constancy restricts pollinators from visiting less optimal flowers.
The formation of floral constancy in individual pollinators is not a fixed action pattern, but it is not a completely labile or random choice either.
Multiple experiments have shown that the flower morphs that bees develop their constancy towards are a result of the availability of the flower, the quality of its nectar, the ease of extracting food from those flowers, even the weight of the load that has to be carried back to the hive. In essence, the bees are delicately balancing opportunity and handling costs as they forage for the hive.
Beyond being a handy tool for understanding the natural world, OFT can be useful in restoration projects as well. In Yellowstone National Park, for instance, it was used to understand carrying capacities. Ripple and Beschta (2004), applied OFT to understand the management of northern range ungulate herds after the reintroduction of wolves into Yellowstone National Park. Recommending that future restoration projects focus on the recovery of natural processes instead of active management. The introduction of wolves had made it important to regenerate historical ungulate migration routes that had less predation pressure.
By accounting for the risks that the ungulates encountered while grazing after introduction of the wolves – aka – the handling costs, the park managers could predict ungulate behaviour better and employ resources effectively for park management.
So OFT has proven useful, but it is not without its criticisms. It is very hard to test, and it has been tested in only a handful of species. We see it across niches from parasites to browsers to carnivores, but it would a lot more to generalise this theory.
So OFT is very useful for a naturalist, but can it help humans too? In a sense it can definitely explain why we love what we love. Extracting calories has always been tough, so no wonder our taste buds love calorie dense foods that are so easily available.
But can it teach us to eat optimally in this new world filled with extreme excesses? I don’t know, but maybe I can test it. Coming tomorrow I will lock up all the junk food in my apartment, while keeping healthy food readily accessible. Maybe the increased handling cost would make junk food less appealing. Or maybe I’ll just manage to irritate myself.
Or maybe I’m not doing it right, maybe I need a real-life wolf to scare me into healthier foraging. Oh, that experiment is just too hard to conduct! I guess I will just accept eating Oreos for now.
Meire, P. M.; Ervynck, A. (1986). “Are oystercatchers (Haematopus ostralegus) selecting the most profitable mussels (Mytilus edulis)?” (PDF). Animal Behaviour. 34 (5): 1427. doi:10.1016/S0003-3472(86)80213-5
Schmid-Hempel, P.; Kacelnik, A.; Houston, A. I. (1985). “Honeybees maximize efficiency by not filling their crop”. Behavioral Ecology and Sociobiology. 17: 61. doi:10.1007/BF00299430.
Lihoreau, M.D., Chittka, L. & Raine, N.E. (2011). Trade-off between travel distance and prioritization of high-reward sites in traplining bumblebees. Functional Ecology, 25: 1284–1292.
Ripple, William J., and Robert L. Beschta. “Wolves and the ecology of fear: can predation risk structure ecosystems?.” BioScience 54.8 (2004): 755-766.